RWTH Aachen Universitypublications.rwth-aachen.de/record/479140/files/479140.pdf · 2015-06-11 · High Product Variety and Company Performance { Organization and Con guration of

High Product Variety and Company Performance – Organization and

Configuration of Strategic Capabilities

Von der Fakultat fur Wirtschaftswissenschaften der

Rheinisch-Westfalischen Technischen Hochschule Aachen

zur Erlangung des akademischen Grades eines Doktors der

Wirtschafts- und Sozialwissenschaften genehmigte Dissertation

vorgelegt von

Moritz Wellige

Berichter: Univ.-Prof. Dr. rer. pol. Frank Thomas Piller

Univ.-Prof. Dr.-Ing. Robert Heinrich Schmitt

Tag der mundlichen Prufung: 19. Mai 2015

Diese Dissertation ist auf den Internetseiten der Universitatsbibliothek online verfugbar.

Zusammenfassung

Das marktliche Umfeld vieler Unternehmen ist heute von kontinuierlichen

Veranderungen gepragt. Zentrale Treiber dieser Veranderungen sind heterogene

Kundenbedurfnisse, unter anderem bedingt durch regional unterschiedliche Kundenan-

forderungen, steigende Nachfrage nach neuen Produktfunktionen und -merkmalen sowie

durchschnittlich kurzer werdende Produktlebenszyklen und Time-to-Market-Intervalle

(van Dolen et al., 2002; Franke et al., 2009; ElMaraghy et al., 2013). Mass Cus-

tomization (MC), oder allgemein variantenreiche Produktstrategien (VPS), werden als

vielversprechende strategische Optionen fur Unternehmen gesehen, deren marktliches

Umfeld von derartigen Veranderungen gepragt ist (Salvador et al., 2009; Fogliatto et al.,

2012; ElMaraghy et al., 2013). Mit steigender Anzahl angebotener Varianten erhohen

sich jedoch vielfach auch die internen produkt- und prozessseitigen Unsicherheiten

sowie die Komplexitat, so dass diese Unternehmen vor der Herausforderung stehen,

organisationale Rahmenbedingungen zu schaffen, die eine effiziente Bereitstellung einer

groen Anzahl von Varianten ermoglichen.

Die wissenschaftliche Literatur zu Mass Customization betont, dass fur die pro-

fitable Umsetzung einer solchen Strategie bestimmte strategische Fahigkeiten erforder-

lich sind (Pine, 1993; Duray, 2002; Salvador et al., 2009; Fogliatto et al., 2012). In

diesem Zusammenhang befassen sich zahlreiche Veroffentlichungen unter anderem mit

der Fragestellung, wie Produktionssysteme und Produktarchitekturen fur Mass Cus-

tomization gestaltet sein mussen (vgl. Fogliatto et al., 2012), oder auch welche spezi-

fischen Fahigkeiten im Bereich der Produktentwicklung fur variantenreiche Produkt-

angebote notwendig sind (Salvador et al., 2009). In diesem Forschungsbereich bietet

die Literatur bereits eine Vielzahl an Erkenntnissen und Implikationen sowohl fur Un-

ternehmen als auch fur die Wissenschaft. Die uberwiegende Zahl der Studien ist dabei

auf Einzelaspekte fokussiert, die fur eine erfolgreiche Umsetzung einer Mass Customiza-

tion Strategie zu berucksichtigen sind. Bislang fehlt jedoch eine umfassende, theoretisch

fundierte Konzeptualisierung der erforderlichen Fahigkeiten fur Mass Customization, die

insbesondere auch verschiedene organisationale Funktionsbereiche einschließt. Weiter-

hin stehen keine geeigneten Messinstrumente zur Verfugung, mit denen diese spezifischen

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Fahigkeiten eines Unternehmens erfasst und bewertet, sowie interorganisationale Ver-

gleiche auf empirischer Basis durchgefuhrt werden konnten. Zudem fehlen bislang Stu-

dien, welche die wechselseitigen Beziehungen zwischen erforderlichen Fahigkeiten sowie

deren interdependente Wirkbeziehungen auf den unternehmerischen Erfolg untersuchen.

Zudem legt die Literatur nahe, dass auch Unternehmen die keine Mass Customization

Strategie verfolgen von der Implementierung einzelner Mass Customization spezifischer

Fahigkeiten profitieren konnen, um erfolgreicher in dynamischen Wettbewerbsumfeldern

agieren zu konnen. Studien die diesen Sachverhalt untersuchen, fehlen jedoch bislang.

Die vorliegende Dissertation tragt dazu bei, die genannten Forschungslucken zu schließen

und leistet damit einen Beitrag zur Verbesserung des Verstandnisses uber Mass Cus-

tomization spezifische Fahigkeiten, deren Zusammenhange und Erfolgswirkungen.

Die Dissertation ist in zwei Teile gegliedert. Der erste Teil widmet sich einer all-

gemeinen Einfuhrung in das Forschungsfeld zu Mass Customization und der theore-

tischen Verortung der Dissertation. Weiterhin werden die Forschungsfragen abgeleitet

und die methodischen Herangehensweisen in den Einzelstudien erlautert. Nach einer

Kurzvorstellung der drei Einzelstudien werden abschließend die Ergebnisse allgemein

diskutiert und der Dissertationsprozess kritisch reflektiert. Im zweiten Teil der vorliegen-

den Arbeit sind die drei Studien als alleinstehende Kapitel angefuhrt. Nachfolgend sind

die Studien kurz zusammengefasst:

Der Ausgangspunkt der ersten Studie ist die theoretische Annahme, dass in dy-

namischen Markten eine strategische Flexibilitat auch fur solche Unternehmen erforder-

lich ist, die keine Mass Customization Strategie verfolgen. Eine Steigerung der Flexi-

bilitat ist jedoch vielfach mit einem Effizienzverlust in operativen Funktionsbereichen

verbunden. In der Studie wird literaturbasiert ein Modell hergeleitet, mit dem unter-

sucht wird, ob der Zielkonflikt zwischen Flexibilitat auf strategischer Ebene und zeit-

bzw. kostenbezogener Effizienz durch spezifische operative Fahigkeiten reduziert werden

kann. Das Modell und die hypothetischen Relationen zwischen den Variablen wurden

unter Verwendung von Befragungsdaten in einem Strukturgleichungsmodell analysiert.

Die Ergebnisse der Analyse zeigen, dass die sogenannte ”mass customization capabil-

ity”, unter der die Fahigkeit zur flexiblen und effizienten Fertigung verstanden wird,

dazu beitragt, den Zielkonflikt zwischen Flexibilitat und Effizienz zu reduzieren.

Die zweite Studie dieser Dissertation widmet sich der Frage, welche spezifischen

Fahigkeiten fur die Umsetzung einer Mass Customization Strategie erforderlich sind

und wie sich diese zusammensetzen. Weiterhin wird in der Studie ein geeigneter

Messansatz identifiziert, der eine Erfassung dieser Fahigkeiten und damit eine Eva-

luation sowie Benchmarking ermoglicht. Ausgehend von Experteninterviews und einer

Literaturanalyse wurden dazu in einem ersten Schritt drei spezifische Fahigkeiten, mit

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je zwei Teildimensionen, identifiziert und definiert. Basierend auf 71 Managementak-

tivitaten und organisationalen Ressourcen wird in einem zweiten Schritt fur jede der drei

Fahigkeiten ein formativer Index entwickelt und empirisch evaluiert. Diese neu entwick-

elten Messinstrumente erlauben Wissenschaftlern und Praktikern neben der Evaluation

der erforderlichen Fahigkeiten auch die unmittelbare Ableitung von Verbesserungsmaß-

nahmen.

In der dritten Studie dieser Dissertation werden die Zusammenhange zwischen den

in der zweiten Studie konzeptualisierten Fahigkeiten fur Mass Customization sowie

deren Einfluss auf den Unternehmenserfolg untersucht. Literaturbasiert wird eine or-

ganisationale Konfiguration abgeleitet, bestehend aus den drei Fahigkeitsdimensionen

als zentrale Komponenten. Die anschließende Analyse von Unternehmensdaten, die

im Rahmen einer großzahligen Befragung erhoben und durch objektive Finanzdaten

erganzt wurden, bestatigt die konzeptionelle Konfiguration und die Relevanz aller drei

Fahigkeitsdimensionen fur die Leistungsfahigkeit und den finanziellen Erfolg von Un-

ternehmen, die eine Mass Customization Strategie verfolgen. Diese Erkenntnisse uber

die Relationen und Erfolgswirksamkeit der drei spezifischen Fahigkeiten tragen dazu

bei, das grundlegende Verstandnis uber Mass Customization Strategien und deren Er-

folgstreiber zu erweitern. Fur die praktische Anwendung bieten die Ergebnisse unter

anderem konkrete Anhaltspunkte fur die Priorisierung von Maßnahmen zum Auf- und

Ausbau der spezifischen Fahigkeiten fur Mass Customization.

Diese Dissertation ist im Rahmen des Graduiertenkollegs ”1491 Anlaufmanagement

- Entwicklung von Entscheidungsmodellen im Produktionsanlauf” an der RWTH Aachen

entstanden. Der Dank fur die Forderung dieses Graduiertenkollegs gilt der Deutsche

Forschungsgesellschaft (DFG).

Summary

Today, business environments are oftentimes characterized by ever-changing market con-

ditions. Major drivers of these changes are increasingly heterogeneous customer needs

due in particular to regionally differing requirements, continuously rising demand for

new product functionalities and features, and shortening product life cycles and time-

to-market periods (Van Dolen et al., 2002; Franke et al., 2009; ElMaraghy et al., 2013).

Mass customization (MC) – more generally referred to as high-variety product strate-

gies (HVPS) – are seen as promising strategic orientations for companies to deal with

such dynamic environmental conditions (Salvador et al., 2009; Fogliatto et al., 2012;

ElMaraghy et al., 2013). The objective of these strategies is to efficiently offer high

variety to meet the heterogeneous needs of customers. However, increasing the number

of available product variants is usually also associated with rising levels of complexity

and uncertainty with regard to processes and products. Companies pursuing a mass

customization strategy are thus forced to create suitable organizational conditions that

enable them to cope with these challenges.

The existing research emphasizes that companies pursuing a mass customization

strategy need to develop and implement distinct strategic capabilities to be able to

achieve a sustainable competitive position (Pine, 1993; Duray, 2002; Salvador et al.,

2009; Fogliatto et al., 2012). For example, there is a large amount of literature ad-

dressing the issue of how manufacturing systems and product architectures need to be

designed to enable companies to efficiently offer customers high variety. Here, flexi-

ble automation technologies and modular product architecture are important enablers

helping to increase the stability and resilience as well as the efficiency of manufacturing

systems (e.g. Fogliatto et al., 2012). The literature also reveals that companies need

to invest in a capability that enables them to efficiently handle the internal and exter-

nal information transfers necessary for product customization (Huffman & Kahn, 1998;

Dellaert & Stremersch, 2005; Franke et al., 2009). Moreover, a capability related to

product development is required that enables companies to develop and continuously

revise a high-variety product offering in order to meet market requirements as precisely

as possible (Salvador et al., 2009).

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Although the research on mass customization provides valuable insights and impli-

cations for companies, most of the studies are focused on single aspects of the capabilities

required to successfully pursue mass customization. The research does not provide a pro-

found theoretical and comprehensive conceptualization of the necessary capabilities for

mass customization, nor have the interrelations between these capabilities or their im-

pact on company performance been investigated in an empirical manner. Furthermore,

there is no measurement approach available that allows companies and researchers to

evaluate and benchmark a company’s ability to efficiently deliver customized products

on a large scale. Besides this, it is suggested in the literature that non-customization

companies that are confronted with dynamic market conditions might also benefit from

implementing distinct mass customization capabilities. However, this has hardly been

studied. By addressing these research gaps, this dissertation aims to help practitioners

and researchers gain a better understanding of mass customization and its relationships

with and impact on company performance.

This dissertation consists of two parts. The first part provides a general introduc-

tion into the field of mass customization based on recent research and related theory,

followed by the identification of the research gaps that represent the motivation for the

research papers of this dissertation, presented subsequently. After a general discussion

of the results, the implications, limitations, and potential avenues for future research are

suggested. In the second part, the three research papers are presented in stand-alone

chapters. Brief summaries of these papers are presented in the following:

The starting point of the first research paper is the theoretical assumption that

even non-customization companies in dynamic business environments need to achieve a

strategic flexibility in order to be able to flexibly reallocate organizational resources to

remain competitive. However, increasing flexibility is frequently associated with a loss

of efficiency on an operational level. Using survey data, I provide empirical evidence

that a mass customization capability, i.e., a capability dedicated to flexible but efficient

manufacturing processes, helps to reduce the tradeoff between strategic flexibility and

organizational efficiency. This paper thus provides a first insight into the relevance of

such an operational capability for company performance.

The second research paper provides two major contributions to research and prac-

tice. First, building on a literature review and interviews with experts of the field, a

comprehensive conceptualization of three strategic capabilities for mass customization

was derived. Second, a formative measurement index was constructed for each of the

three capabilities and evaluated using data from a survey among companies pursuing a

mass customization strategy. The formative indices consist of 71 managerial activities

and organizational resources related to the capabilities for mass customization. These

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indices not only allow researchers and managers to evaluate a company’s ability to effi-

ciently offer high variety, they can also be applied to identify potential for improvements

within a company.

In the third research paper, the interrelations between the three capabilities for

mass customization conceptualized in the second research paper are related to each

other and to company performance. Based on the literature and the contingency theory,

an organizational configuration is proposed consisting of the three strategic capabilities

for mass customization. The derived organizational configuration for mass customiza-

tion was empirically evaluated using survey and financial data from 193 European mass

customization companies. The results emphasize the suggested organizational configu-

ration as well as the relevance of all three mass customization capabilities for company

success. This helps to improve our understanding of mass customization and its drivers

of success. The results also provide implications for companies intending to improve

their mass customization capabilities.

Overall, the results of the three research papers help to improve our understanding

of the organizational capabilities required to successfully pursue mass customization as

well as the relationships among them and their impact on company performance.

This dissertation is part of the ”Graduiertenkolleg 1491 Anlaufmanagement: En-

twicklung von Entscheidungsmodellen im Produktionsanlauf” at the RWTH Aachen

University. We are very grateful for the founding of the Graduiertenkolleg provided by

the German Research Foundation (Deutsche Forschungsgesellschaft (DFG)).

Thesis structure overview

This dissertation consists of two main parts. Part I outlines the theoretical background,

the existing research gaps, the methodological research approach, and concludes with a

general discussion. The second part comprises three independent research papers that

address issues related to the need and performance implications of specific capabilities

for mass customization and high variety production strategies.

Previous versions of the three papers were presented at academic research seminars,

and/ or international peer-review conferences. A later version of paper II is submitted to

an academic journal and currently under review, and paper III is intended for publication

in a peer-reviewed journal as well. The following table provides an overview about awards

received as well as the seminars, conferences, and journals at which the three papers were

submitted and/ or accepted.

Paper I: Bridging strategic flexibility and operational efficiency: The

mediating role of operational capabilities

• TIM doctoral seminar, May 2014, RWTH Aachen University.

Paper II: Measuring firms’ capabilities for mass customization:

Construction of a formative measurement index

• Mass Customization Workshop 2013, Politecnico di Milano, February 2013, Mi-

lano, Italy.

• 3rd IMR PhD day, Radboud University Nijmegen, May 2013, Nijmegen, the

Netherlands.

• 7th World Conference on Mass Customization, Personalization, and Co-Creation

2014, February 2014, Aalborg, Denmark.

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• Steiner, F. and M. Wellige (2014). Strategic Capabilities to Manage High-Variety

Production Environments: The Role of Underlying Activities and Organizational

Resources. Proceedings of the 7th World Conference on Mass Customization,

Personalization, and Co-Creation (MCPC 2014), Aalborg, Denmark, February 4th

- 7th, 2014. T. D. Brunoe, K. Nielsen, K. A. Joergensen and S. B. Taps, Springer

International Publishing: 487-504. (early version)

• Academy of Management Meeting 2014, August 2014, Philadelphia, United States

of America.

• Nominated for the Best Student Paper Award 2014 of the Operations Management

Division of the Academy of Management.

• Accepted for the publication in the Best Paper Proceedings of the Academy of

Management Meeting 2014.

• Later version of this paper is already in the publishing process.

Paper III: High-variety product offerings and company performance: The

mediating roles of robust process design and interaction competence

• TIME doctoral seminar, May 2014, RWTH Aachen University.

• Paper will be submitted to a peer-reviewed journal for publication.

Contents

Zusammenfassung iii

Summary vii

Thesis structure overview xi

Contents xiii

List of Figures xv

List of Tables xvii

Abbreviations xix

I High product variety and company performance: Theoreticalbackground, results, and reflections 1

Introduction 3

Theoretical background, literature review, and research objectives 7

2.1 Mass customization and organizational capabilities . . . . . . . . . . . . . 7

2.2 Theoretical background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2.1 Resource-based view of the firm . . . . . . . . . . . . . . . . . . . . 14

2.2.2 Contingency theory . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3 Research objectives and questions . . . . . . . . . . . . . . . . . . . . . . . 16

Summary of the research papers 19

3.1 Research paper I: Bridging strategic flexibility and operational efficiency:The role of operational capabilities . . . . . . . . . . . . . . . . . . . . . . 21

3.2 Research paper II: Measuring firms’ capabilities for mass customization:Construction of a formative measurement index . . . . . . . . . . . . . . . 22

3.3 Research paper III: High-variety product offerings and company perfor-mance: The mediating roles of robust process design and interaction com-petence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

General discussion and conclusion 27

4.1 Theoretical implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.2 Managerial implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.3 Limitations, reflections, and future research . . . . . . . . . . . . . . . . . 33

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4.3.1 Data, sample size, and common method bias . . . . . . . . . . . . 33

4.3.2 Mediation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.3.3 Formative measurement approach . . . . . . . . . . . . . . . . . . 38

4.3.4 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

II Research Papers 63

1 Bridging strategic flexibility and operational efficiency: The role ofoperational capabilities 65

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Theoretical background and hypotheses . . . . . . . . . . . . . . . . . . . . . . 69

Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Discussion and implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

2 Measuring firms’ capabilities for mass customization: Construction ofa formative measurement index 97

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Mass customization - a strategy for high-variety markets . . . . . . . . . . . . . 101

Developing a formative index for mass customization . . . . . . . . . . . . . . . 109

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

3 High-variety product offerings and company performance: The medi-ating roles of robust process design and interaction competence 187

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

Theory and hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

Discussion and implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

List of Figures

Part II 65

1.1 Hypothesized model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

1.2 Results of the structural equation model estimation . . . . . . . . . . . . . 80

3.1 Hypothesized model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

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List of Tables

Part I 31.2 Development of product variety and product lifecycles between 1997 and

2012 (Kwasniok & Kilimann, 2012) . . . . . . . . . . . . . . . . . . . . . . 4

2.3 Overview of research papers related to resources and capabilities for masscustomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.4 Overview of research questions, research papers, and methodological ap-proaches applied in this thesis . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.5 Overview of main research objectives and results of this thesis . . . . . . . 44

Part II 65

1.1 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

1.2 Results of the measurement validation . . . . . . . . . . . . . . . . . . . . 78

1.3 Evaluation of the measurement constructs . . . . . . . . . . . . . . . . . . 79

1.4 Results of the PLS-SEM estimation . . . . . . . . . . . . . . . . . . . . . . 80

1.5 Results of the mediation analyses . . . . . . . . . . . . . . . . . . . . . . . 81

2.1 Five steps of formative index development as used in this study . . . . . . 110

2.2 Definition of solution space development and its sub dimensions . . . . . . 114

2.3 Definition of robust process design and its sub dimensions . . . . . . . . . 118

2.4 Definition of interaction competence and its sub dimensions . . . . . . . . 121

2.5 Reconsideration of the strategic capabilities framework . . . . . . . . . . . 122

2.6 Distribution of respondents’ positions in the survey sample . . . . . . . . 126

2.7 Distribution of industries represented in the survey sample . . . . . . . . . 126

2.8 Distribution of countries of origin in the survey sample . . . . . . . . . . . 126

2.9 Classification of the degree of customization according to the categoriesdefined by Lampel & Mintzberg (1996) . . . . . . . . . . . . . . . . . . 127

2.10 Measurement model results on construct level . . . . . . . . . . . . . . . . 130

2.11 Measurement model results on item level . . . . . . . . . . . . . . . . . . . 131

2.12 Indicator correlations in the SSD model . . . . . . . . . . . . . . . . . . . 133

2.13 Indicator correlations in the RPD model . . . . . . . . . . . . . . . . . . . 134

2.14 Indicator correlations in the IC model . . . . . . . . . . . . . . . . . . . . 135

2.15 Indicator contribution to the formative hierarchical latent constructs . . . 139

2.16 PLS estimation for the MIMIC models . . . . . . . . . . . . . . . . . . . . 140

2.17 Results for the multi-factor models . . . . . . . . . . . . . . . . . . . . . . 141

2.18 Resulting formative measurement indices . . . . . . . . . . . . . . . . . . 141

3.1 Distribution of respondents’ positions . . . . . . . . . . . . . . . . . . . . 202

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3.2 Distribution of surveyed companies across NACE divisions . . . . . . . . . 202

3.3 Descriptive statistics and correlations . . . . . . . . . . . . . . . . . . . . . 208

3.4 Results of the hierarchical OLS regression analyses . . . . . . . . . . . . . 210

3.5 Results of OLS regression analyses for mediating variables and RoS . . . . 211

3.6 Results of the mediation analyses . . . . . . . . . . . . . . . . . . . . . . . 213

Abbreviations

AGFI Adjusted goodness of fit index

ASSD Adaptive solution space development

ASV Average shared variance

AVE Average variance extracted

B2B Business-to-business

B2C Business-to-consumer

CB Covariance-based

CBP Cost-based performance

CFA Confirmatory factor analysis

CFI Comparative fit index

CI Confidence interval

CN Choice navigation

CR Composite reliability

d.f. Degrees of freedom

DFG Deutsche Forschungsgesellschaft

EFA Exploratory factor analysis

EIC External interaction competence

GFI Goodness of fit index

GICS Global industry classification standard

HVPS High-variety product strategy

HPV High product variety

IC Interaction competence

IIC Internal interaction competence

ISSD Initial solution space development

MC Mass customization

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MCC Mass customization capability

MIMIC Multiple indicators and multiple causes model

ML Maximum likelihood

MSV Maximum shared variance

NNFI Non-normed fit index

NFI Normed fit index

OE Operational efficiency

OLS Ordinary least squares

PDPR Process design for process robustness

PLS Partial least squares

PR Process robustness

QFD Quality function deployment

RBV Resource-based view

RMSEA Root mean square error of approximation

RoS Return on sales

RPD Robust process design

RWTH Rheinisch-Westfalische Technische Hochschule

SD Standard deviation

SE Standard error

SEM Structural equation modeling

SF Strategic flexibility

SRMR Standardized root mean square residual

SSD Solution space development

TBP Time-based performance

TC Technological capability

VB Variance-based

VIF Variance inflation factor

VPS Variantenreiche Produktstrategien

WTP Willingness-to-pay

Part I

High product variety and

company performance:

Theoretical background, results,

and reflections

1

Introduction

Over the last decades, driven by the globalization of markets and growing sophistication

of customers, the former one-product-fits-all assumption has become outdated for many

companies. Their business environments are frequently characterized by high dynamics,

as they are confronted with regionally and culturally differing customer preferences

that are changing increasingly quickly, continuously rising demand for new product

functionalities and features, as well as shortening product life cycles and time-to-market

periods (van Dolen et al., 2002; Franke et al., 2009; ElMaraghy et al., 2013). As a result

of these changes, the number of product variants has steadily increased. A recent cross-

industry study revealed that the number of product variants across different industries

(automotive, chemicals, machinery, fast moving consumer goods, and pharmaceuticals)

in 2012 was equal to 220% of the number of sales products in 1997, the number thus

has more than doubled in only 15 years (Kwasniok & Kilimann, 2012), see Table 1.2.

During the same period of time, the average product life cycle was reduced to 76% of

the 1997 level (Kwasniok & Kilimann, 2012). Thus, companies in such industries can no

longer profitably meet customer preferences by means of mass-manufactured standard

products which are offered in only a few variants; instead, they are forced to implement

new strategic approaches in order to cope with these challenges.

Mass customization (MC) is regarded as a promising strategic approach for compa-

nies to gain competitive advantage in markets shaped by highly heterogeneous customer

preferences and the challenges discussed above (Gownder et al., 2011; Fogliatto et al.,

2012; ElMaraghy et al., 2013). The term ”mass customization” was introduced by Davis

(1987) in the mid-1980s and was significantly advanced by Pine and his colleagues (Pine,

1993; Pine et al., 1993; Pine, 1995). They describe mass customization as a strategic

approach that aims to efficiently offer a large number of product variants and individ-

ual customization to meet the heterogeneous needs of customers. Promoted by flexible

3

4

advanced manufacturing technologies and the emergence of new information and com-

munication technologies, mass customization is regarded as a highly relevant product

strategy (Gownder et al., 2011).

Table 1.2: Development of product variety and product lifecycles between 1997 and2012 (Kwasniok & Kilimann, 2012)

Industry Number of salesproducts in 2012related to 1997

Duration of productlifecycles in 2012 re-lated to 1997

Chemical industry 313% 63%Pharmaceutical industry 223% 92%Machinery industry 216% 81%Automotive industry 170% 93%Fast moving consumer goodsindustry

162% 54%

However, building customizable products that allows nearly everyone to find exactly

what they want has proven difficult to achieve profitably at scale, reflected in ample ex-

amples of companies failing to succeed pursuing with such a product customization strat-

egy (Gownder et al., 2011). Addressing this issue, the literature reveals that successfully

pursuing and implementing this strategy requires specific organizational capabilities. In

recent research, Salvador et al. (2009) suggest a capabilities framework consisting

of three specific capabilities required to successfully run a mass customization strategy.

Solution space development is located in the functional area of product development

and is associated with the development of a product offering that fulfills customers’

heterogeneous needs. The robust process design capability is dedicated to operations

management and efficient manufacturing of products with many different variants. The

third capability, choice navigation, is located in the functional area of marketing and

sales and reflects a company’s ability to help its customers identifying a suitable product

solution while minimizing the perceived complexity and burden of choice.

Notwithstanding the importance of identifying these capabilities, past research has

not clearly defined and conceptualized these capabilities, nor has it provided an appro-

priate measurement approach or sufficient evidence of the interrelations and performance

effects of these specific capabilities. Hence, there is a need to investigate these capa-

bilities in a more detailed way. A more comprehensive understanding of the required

5

capabilities is crucial for companies implementing mass customization or trying to im-

prove their ability to customize products. In this context, my dissertation thesis aims

to contribute to the literature.

Next to this introductory chapter, there are three additional chapters and a separate

second part (Part II) containing three stand-alone research papers. In the following

chapter, the theoretical background of this thesis and a comprehensive literature review

on mass customization are presented. Subsequently, the research agenda of this thesis

is derived by identifying research gaps, defining the research objectives, and formulating

associated research questions. In the next chapter, the research questions are related to

the corresponding research paper. An overview of the methodological approaches applied

in this dissertation is discussed, followed by short summaries of each of the three research

papers. The fourth chapter closes this first part of the thesis by summarizing and

discussing the major results of the papers, their theoretical and managerial implications

and suggestions for future research, and presenting the conclusion. The second part of

this dissertation consists of the three research papers that address the previously defined

research objectives.

Theoretical background,

literature review, and research

objectives

2.1 Mass customization and organizational capabilities

Today, the term ”mass customization” is used not only for various product strategies

related to high-variety or flexible manufacturing, but also for assortment matching con-

cepts and customized services. Thus, different kinds of strategies and approaches focused

on profiting from heterogeneous needs among customers are subsumed under the con-

cept of mass customization (Piller & Tseng, 2010). This is also reflected in the large

amount of literature on mass customization, which provides various competing defini-

tions, so that a clear conceptualization of the boundaries of mass customization is also

still missing (Duray et al., 2000; Piller, 2004; Piller et al., 2005; Fogliatto et al., 2012).

Hence, it is necessary to focus the term and derive a definition of mass customization

that serves the purposes of this thesis.

I follow the initial understanding of mass customization popularized by Davis (1987)

which was later supplemented by other researchers. Davis (1987) describes mass cus-

tomization as a concept dedicated to reaching the same large number of customers as in

mass markets while treating each customer individually as in the customized markets of

the pre-industrial area. More precisely defined, mass customizers provide customizable

products such that nearly each customer finds a product that meets their individual

needs exactly, at a price comparable to that of mass-produced standard goods (Pine,

1993; Tseng et al., 1996; Tseng & Jiao, 2001). It is crucial to recognize the aspect

7

8

that mass customization companies have to limit their offered variety in order to be

able to gain efficiency levels comparable to those of mass production systems (Piller,

2004), as increasing the offered variety usually leads to increment manufacturing com-

plexity, which has an inherent danger of increasing costs on operational level (Blecker

& Abdelkafi, 2006; ElMaraghy et al., 2013). Thus, mass customizers have to develop a

product offering that allows them to address a broad range of customers with diverse

needs and is at the same time efficient to produce (Pil & Holweg, 2004).

The idea of mass customization is based on the assumption that customers gain

a higher utility from these customized products compared to standardized products

(Chamberlin, 1962; Piller, 2004), whereby higher levels of product utility are associ-

ated with an increment in customers’ willingness to pay a price premium (Franke &

Piller, 2004), which might increase a company’s revenue (Pine, 1993). Overall, mass

customization can be regarded as an approach that regards heterogeneity in customer

needs as an opportunity for companies to realize new revenue streams, which is contrary

to the conventional view according to which diversity of needs is oftentimes seen as a

threat (Piller & Steiner, 2013). The key and constituting element of mass customiza-

tion strategies is the integration of customers into the value creation processes in order

to define, configure, or modify their individual solution (Piller, 2004). Depending on

the customer order decoupling point (Rudberg & Wikner, 2004), i.e., the stage of the

value chain at which the customers are integrated and the product becomes customer-

specific, different kinds of mass customization strategies can be distinguished (Lampel

& Mintzberg, 1996), whereby decoupling points located further upstream in the value

chain are typically associated with higher levels of uncertainty in operations and require

different levels of process adaptions compared to a mass production system (Rudberg &

Wikner, 2004).

In the last decade, the opportunities, potentials, and advantages of mass cus-

tomization have been documented in ample scientific papers (e.g. Salvador et al., 2009;

Brabazon et al., 2010; Fogliatto et al., 2012). Moreover, the applicability and relevance

of mass customization are demonstrated by various examples of successful companies in

different industries, such as the APC, Festo, NBIC, HP, Chrysler, or Dell (Comstock

et al., 2004; Partanen & Haapasalo, 2004; Hvam, 2006; Lu et al., 2009; Daaboul et al.,

2012; Steiner, 2014). However, it is striking that there are also many examples of costly

failures, especially by established companies (Gandhi et al., 2014) such as Levi Strauss.

9

By investigating why some mass customizers fail while others perform successfully, it

is frequently emphasized that implementing or changing towards mass customization

requires the development and application of specific technologies, methods, or organi-

zational capabilities (Salvador et al., 2009; Huang et al., 2010; Fogliatto et al., 2012).

Accordingly, Brown and Bessant (2003) state that the implementation or move to-

wards mass customization does not just happen – it can only be achieved by means of

a strategic development and acquisition of a range of mainly internal resources such as

technologies, methods, or routines, and their purposeful linkage.

In the extensive body of literature in the field of mass customization, there is ample

conceptual and empirical work on customer integration techniques, modular design tech-

niques, flexible manufacturing systems, and supply chain management methods (Smith

et al., 2013). However, a review of a selection of frequently cited conceptual and em-

pirical papers on mass customization published since 20001 reveals that the papers are

focused – almost without exception – on one aspect, one phase of the value chain, or

other isolated success factors or best practices for mass customization. Table 2.3 pro-

vides an overview of the reviewed articles as well as the applied methodological approach

and the specific aspect which is the subject of the corresponding article.

Most of the reviewed papers address issues of product design, manufacturing, or

related operations. They deal with the antecedents of a mass customization-specific

manufacturing capability as well as with the planning, configuration, and optimization

of flexible manufacturing systems in order to achieve the required operational perfor-

mance. Moreover, there are some papers dealing with supply chain configuration and

management for mass customization, and others relating to product architecture, mod-

ularity, and platform design for mass customization.

Besides articles about manufacturing and product design for mass customization,

there is ample work relating to marketing, sales, order fulfillment, and the associated

customer interaction and customer integration into value creation processes. Some pa-

pers regard the product customization processes from the customer perspective in terms

of process utilities but also in terms of the potential problem of choice complexity. Build-

ing on these, other papers provide design recommendations for product configuration

1Selection strategy: Google scholar search. Search strings: term: ”mass customization”, since 2000.Selection of A-C-ranked peer-reviewed journals (according to the ”Jourqual-Ranking 2.1”); This list wassupplemented by a few papers that has been cited in recent review studies on mass customization.

10

processes and related order processing systems. Finally, a third group of papers relates

to the development of product offerings for mass customization. Here, most of the pa-

pers deal with the identification of relevant product attributes as the starting point for

deriving a product offering that meets customer heterogeneity. Furthermore, a number

of other papers provide approaches to define a high-variety product offering.

This review provides some insights and shortcomings of the research on mass cus-

tomization: (1) The literature in the field of mass customization is characterized by

a high level of fragmentation; (2) some aspects have gained considerable attention in

recent years (e.g., manufacturing and product design), while other subject areas have

been largely neglected (e.g., development of appropriate product offerings for customers

with heterogeneous needs); (3) almost all articles are focused on a single aspect; (4)

some of the aspects or technologies related to mass customization might also help non-

customizers to cope with dynamic business environments.

Table 2.3: Overview of research papers related to resources and capabilities for masscustomization

Paper [Methodology] Regarded MC specific aspect/technology/method/etc.

Papers related to product design/operations and manufacturing/supply chain management

Aigbedo (2007) [s] Supply chain managementAigbedo (2009) [c] Supply chain managementAldanondo & Vareilles (2008) [c] Product and process configurationBlecker & Abdelkafi (2006) [c] Variety managementBlecker et al. (2006) [c] Metrics approach for managing complexityBlecker & Abdelkafi (2007) [c] Product component commonality metricsBrabazon et al. (2010) [s] Flexible reconfigurable fulfillment systemsBrown & Bessant (2003) [cs] Agile manufacturing strategiesComstock et al. (2004) [ccs] Order decoupling point and production system flexibilityCoronado et al. (2004) [ccs] Supply chain managementDaaboul et al. (2011) [c] Product and process varietyDean et al. (2009) [ccs] Manufacturing resource planningDietrich et al. (2007) [ccs] Information systems for managing complexityDuray et al. (2000) [e] Manufacturing system, product design, order decoupling pointDuray (2002) [e] Manufacturing strategies/process planningDuray (2004) [e] Production planningElMaraghy (2005) [c] Reconfigurable Manufacturing SystemsElMaraghy et al. (2013) [c] Product variety managementHou et al. (2007) [ccs] Supply chain managementHsuan Mikkola & Skjøtt-Larsen(2004) [c]

Supply chain management

Huang et al. (2007) [s] Product platform configuration and supply chain managementHuang et al. (2008) [e] Process implementation and MC manufacturing capabilityHuang et al. (2010) [e] Organizational structure and MC manufacturing capabilityIsmail et al. (2007) [c] Manufacturing agility and product flexibilityJiao et al. (2007a) [ccs] Process platform planing and product and process varietyJiao et al. (2007b) [ccs] Process configuration and operational routines

(continued on next page)

11

(continued from previous page)

Khalaf et al. (2010) [e] Product family design and supply chain managementKamali & Loker (2002) [e] Customers’ design involvement in product configurationKristal et al. (2010) [e] Antecedents of MC manufacturing capabilityLabarthe et al. (2007) [s] Supply chain managementLai et al. (2012) [e] Supply chain management and MC manufacturing capabilityLiao et al. (2013) [e] Antecedents of MC manufacturing capabilityLiu et al. (2006) [e] Work-design practices and MC manufacturing capabilityLiu et al. (2010) [e] Demand/supply uncertainties and MC manufacturing capabilityLiu et al. (2012) [e] Functional integration and operational performanceLiu & Deitz (2011) [e] Supply chain managementMacCarthy et al. (2003) [ccs] Modes of operationsMatthews et al. (2009) [ccs] Process configurationMikkola (2007) [c] Management of product architecture modularityNi et al. (2007) [cs] Supply chain managementPallari et al. (2010) [cs] Additive manufacturing technologies for customized productsPartanen & Haapasalo (2004) [ccs] Fast production approachPeng et al. (2011) [e] IT systems and MC manufacturing capabilityPotter et al. (2004) [ccs] Supply chain management/vendor managed inventoryRudberg & Wikner (2004) [c] Customer order decoupling point and process designSalvador & Forza (2004) [e] PCP and order acquisition and fulfillment processSalvador et al. (2004) [cs] Configuration of supply chainsSalvador et al. (2008; 2009) [e][c] Relevance of a robust process design capability for MCShao & Ji (2008) [c] Postponement strategiesSkipworth & Harrison (2006) [ccs] Form postponementSquire et al. (2006) [e] Product customisation and competitive prioritiesStump & Badurdeen (2012) [ccs] Lean manufacturingSu et al. (2005) [s] Postponement structuresTien (2006) [c] Supply and demand chain managementTu et al. (2001) [e] Time-based manufacturing practicesTu et al. (2004a) [e] Manufacturing practicesTu et al. (2004b) [e] Modularity-based manufacturingTu et al. (2007) [ccs] Product development cost estimationTu et al. (2009) [ccs] RFID technology in manufacturing and logisticsTuck et al. (2008) [cs] Rapid manufacturingvan Hoek (2000) [e] Supply chain managementVinodh et al. (2010) [ccs] Agile manufacturing conceptsWan et al. (2012) [e] Product variety and operations/sales performanceWang (2009) [c] Evaluation approach for manufacturing agilityWikner et al. (2007) [s] Manufacturing control systemsYao & Liu (2009) [c] Supply chain managementZangiacomi et al. (2004) [ccs] Process planning and schedulingZhang & Tseng (2007) [c] Cost implications of product and process designZhang et al. (2014) [e] Antecedents to MC manufacturing capabilityZhong et al. (2013) [c] RFID for manufacturing systems

Papers related to marketing/sales/order processing in MC

Aydin & Gungor (2005) [c] Customer specific information processingChang & Chen (2009) [e] Customer response to web-based customizationsChang et al. (2009) [e] Customer participation and product satisfactionDa Cunha et al. (2010) [c] Selection of product modules and customers’ needsDellaert & Stremersch (2005) [e] PCP, utility, and complexityDellaert & Dabholkar (2009) [e] Services, options, and consumers intention to use MCFelternig (2007) [c] Configuration knowledge representationFiore et al. (2004) [e] Individual differences, motivations, and willingness to use MCFiogliatto & da Silveira (2008) [ccs] Design of PCPForza & Salvador (2002) [ccs] Information systems for order acquisition/fulfillment processesForza & Salvador (2008) [c] Information systems for sales and technical configurationsFranke & Piller (2004) [e] Utility of PCP


12


Franke & Schreier (2008) [e] Utility of self-designed productsFranke & Schreier (2010) [e] Design requirements for PCPFranke et al. (2008) [e] Customer-customer interaction and PCPFranke et al. (2009) [e] Design requirements for PCPFranke et al. (2010) [e] Utility of PCPFrutos et al. (2004) [c] Decision support system for PCPKrishnapillai & Zeid (2006) [c] Mapping of product design specificationsLiechty et al. (2001) [e] Design requirements for PCPMatzler et al. (2011) [e] Customer confusion in online PCPMavridou et al. (2013) [c] Recommendation system for PCPMerle et al. (2010) [e] Attributes of customers’ perceived value (product/process)Ninan & Siddique (2006) [ccs] Framework for customer integration in PCPOng et al. (2006) [ccs] Web-based configuration system for PCPPiller et al. (2005) [cs] Mass confusion and collaborative customer co-designSalvador et al. (2008; 2009) [e][c] Relevance of a choice navigation capability for MCSchreier (2006) [e] Utilitarian and hedonic benefits of customized productsTseng et al. (2005) [c] Customer specific information processingValenzuela et al. (2009) [e] Design requirements of PCPXie et al. (2005) [c] Designing product configurations with complex constraints

Papers related to the development of high variety product offerings for MC

ElMaraghy et al. (2013) [c] Approaches for defining high variety product offeringsFranke & Schreier (2008) [e] Using customer feedback for product offering evaluationHermans (2012) [c] Constructing a product offeringKaplan et al. (2007) [e] Relevance of base-category product factors for product offeringsMacCarthy et al. (2002) [c] Key value attributes of productsPil & Holweger (2004) [e] Product variety and order-fulfillment strategiesSalvador et al. (2008; 2009) [e][c] Relevance of a solution space development capability for MCZhang & Tseng (2007) [c] Identification of relevant product attributes for customization

Notes. c = conceptual. e = empirical. s = simulation. cs = case study.ccs = conceptual/case study. PCP = product configuration process

Salvador et al. (2008; 2009) have condensed the broad and fragmented liter-

ature on mass customization by proposing a framework of three strategic capabilities

for mass customization: (1) solution space development (SSD), (2) robust process design

(RPD), and (3) choice navigation (CN). The techniques, practices, and other attributes

of mass customization identified during the literature review are reflected in these ca-

pabilities: Solution space development refers to a company’s ability to identify those

product attributes along which customer needs differ most in order to define the range

of product variants that the company will offer – but also what it will not deliver to its

customers. Furthermore, the SSD capability is also associated with the ability to revise,

trim, or extend an existing product offering if necessary. Robust process design refers

to the ability to reuse or recombine existing organizational and supply chain resources

to be able to provide customized products with near-mass production efficiency and

reliability. This requires flexible manufacturing systems that allow the production of

smallest batch sizes of product variants with short delivery times and at a price point

13

comparable to mass-produced products. Thus, it aims to maintain the stability of man-

ufacturing and supply chain processes and mitigate additional costs that might arise for

high variety. Choice navigation refers to the ability to support customers in identifying

their needs and the best-fitting solution while minimizing the complexity and burden of

choice associated with the customization process. This requires intense communication

and interaction between the customers and the company, oftentimes realized in so-called

product configuration processes.

Notwithstanding the valuable contribution of this mass customization strategic ca-

pabilities framework, which provides a comprehensive picture of mass customization

companies as well as a taxonomy of the required capabilities, it remains vague and pro-

vides little empirical evidence or even detailed information on constituting technologies,

practices, or other aspects underlying these capabilities. As the competitive advantage

from pursuing mass customization depents not only on the ability to develop and imple-

ment mass customization-specific technologies, methods, or resources, but also on the

ability to coordinate and leverage them simultaneously, it remains rather difficult to de-

rive strategic guidance for implementing or improving mass customization businesses as

a whole based on this framework. Furthermore, it is an enduring problem for companies

to develop appropriate organizational designs for increasingly complex and dynamic sit-

uations (van de Ven et al., 2013), which is especially true for mass customization. Thus,

it is necessary to gain a deeper understanding of the required capabilities, their consti-

tuting elements,and their interrelationships. Besides this, the literature review suggests

that non-customizers may also benefit from implementing mass customization manu-

facturing principles in order to cope with dynamic business environments, which has

not, however, been exhaustively investigated yet. Before deriving the research gaps and

objectives in a detailed way, I provide in the following an overview of the theories on

which this thesis is based.

2.2 Theoretical background

This thesis relies mainly on two major theories of strategic management and organiza-

tional research: the resource-based view of the firm and the contingency theory. The

resource-based view of the firm represents an appropriate theory in the context of this

thesis as it regards companies as bundles of resources that determine competitive edge,

14

thereby reflecting the relevance of different organizational aspects (e.g., technologies,

knowledge, or practices) which also is in line with the results of the above presented lit-

erature review on MC. The contingency theory suggests that companies need to achieve

a fit between their organizational resources and the business environment. This reflects

research on mass customization that emphasizes the importance of achieving internal

fit (e.g. McCarthy, 2004) and external fit, i.e., aligning the organization with customer

needs (e.g. Salvador et al., 2009). As these theories are comprehensively discussed and

defined in the literature, I only provide brief overviews of their main underlying ideas

and assumptions.

2.2.1 Resource-based view of the firm

The ranges of mass customization-specific technologies or practices as well as the pro-

posed three capabilities are regarded as valuable and critical for the success of mass cus-

tomization businesses (Duray, 2002; Piller, 2004; Rungtusanatham & Salvador, 2008).

This is in line with the resource-based view of the firm, which regards a company’s in-

ternal resources as the critical source of success. Penrose (1959) was the first to state

that companies are bundles of resources which have to be effectively combined in order

to increase competitiveness. However, the resource-based view of the firm was popular-

ized mainly by Wernerfelt (1984) and was further developed in the 1990s by different

researchers (e.g. Barney, 1991; Peteraf, 1993; Hayes & Pisano, 1996; Teece et al., 1997).

According to this broadly accepted theory in strategic management, differences in the

performance and success of companies are the result of a heterogeneous distribution of

resources across companies (Wernerfelt, 1984; Teece et al., 1997; Eisenhardt & Martin,

2000). In this reasoning, it is not only the bundle of internal resources that determines

the ability of companies to gain a competitive advantage but also whether other com-

panies lack such resources (Amit & Schoemaker, 1993). By identifying those resources

that allow a resource position barrier to be implemented and sustained, companies are

able to generate above-average returns (Wernerfelt, 1984).

Resources are defined as tangible and intangible assets possessed and controlled by

or accessible to a company (Helfat & Peteraf, 2003). Wernerfelt (1984) introduces a

broad definition of resources. Everything that could strengthen or weaken a company’s

15

position can be regarded as a resource. Thus, machinery, effective procedures, accessi-

ble knowledge of technology, trademarks, trade contracts, and skilled staff are potential

resources (Wernerfelt, 1984). To the discussion on resources, Barney (1991) adds that

they need to meet four criteria to generate sustained competitive advantage. Accord-

ingly, resources have to be valuable, rare, inimitable, and non-substitutable. However,

bundles of valuable resources by themselves are not sufficient for competitive advan-

tage (Eisenhardt & Martin, 2000), which is also dependent on a company’s ability to

coordinate and leverage resources in a meaningful way (Makadok, 2001). Thus, besides

resources, capabilities are additionally regarded as crucial in explaining performance and

competitiveness (Barney, 1991; Amit & Schoemaker, 1993). They are defined as ”a firm’s

capacity to deploy resources” (Amit & Schoemaker, 1993, p. 35). Company-specific ca-

pabilities frequently depend on routines that are intangible, although they might be man-

ifested in tangible products and processes (Leonard-Barton, 1992). Makadok (2001, p.

389) states that ”a capability is a special type of resource – specifically, an organization-

ally embedded nontransferable firm-specific resource whose purpose is to improve the

productivity of the other resources possessed by the firm.” Thus, it is difficult to acquire

capabilities; instead, companies need to develop and build them themselves (Teece et al.,

1997). Therefore, mass customizers need to have specific resources such as manufactur-

ing technology available, and furthermore need to build these capabilities to utilize them

in a purposeful way in order to become a successful.

2.2.2 Contingency theory

Beside the resource-based view of the firm, which provides a meaningful explanation for

the need for as well as the exploitation of mass customization-specific resources and capa-

bilities, the literature suggests that companies also need to accomplish a fit between their

organization and existing environmental demands in order to achieve high performance

(Burton, 2004). Therefore, this thesis also relies on the contingency theory. The central

proposition of this theory is ”that performance outcomes of an organizational unit are a

result of the fit between the unit’s external context and internal arrangements” (van de

Ven et al., 2013, p. 394). As part of the contingency theory, the configurational per-

spective provides a holistic view on the context and design of companies (Venkatraman,

1989; Donaldson, 2001), according to which companies are regarded as configurations

16

consisting of sets of subcomponents which are related to each other in a way that re-

sults in a consistent system (Miller, 1986, 1992). In analyzing organizational designs,

the configurational perspective is not focused on the single elements of an organization;

it rather emphasizes how a work system is designed in terms of the interaction of its

elements taken together as a whole (van de Ven et al., 2013).

Vorhies and Morgan (2003) states that each strategy can be related to one ideal

organizational configuration that enables companies to improve their performance and

achieve their strategic objectives. However, as organizational designs and the exter-

nal environments of companies consist of various elements which are partly conflicting,

achieving a perfect fit between internal and external organizational factors is nearly

unattainable (Miller, 1993; van de Ven et al., 2013). Moreover, in contexts that are

characterized by multiple conflicting environmental demands and internal conflicting

goals, it becomes difficult to identify a single valid organizational configuration (van de

Ven et al., 2013). Instead, in a distinct strategic setting, a limited number of organi-

zational configurations for companies will prevail, which can be used to describe large

proportions of companies that achieve high performance (Wiklund & Shepherd, 2005).

Companies should therefore strive to achieve high levels of internal consistency and

a good fit with contextual factors by attempting to identify feasible sets of internally

consistent patterns in order to enhance their performance (Miller, 1981; Venkatraman,

1989; Ketchen et al., 1993; Miller, 1996). Mass customization companies must therefore

internally align the strategic capabilities and related resources required to pursue a mass

customization strategy and achieve a competitive edge. Moreover, they need to accom-

plish a high fit between these capabilities, their constituting resources and environmental

demands, such as customers’ heterogeneous needs.

2.3 Research objectives and questions

The previous chapter has shown that the research on mass customization lacks a suffi-

cient definition and conceptualization of the strategic capabilities for mass customiza-

tion, insights related to the interrelations between these capabilities, and their effects on

company performance. This section is dedicated to identifying the research gaps more

closely and to deriving the research questions.

17

The first objective of this thesis relates to the mass customization manufacturing

capability (termed as ”mass customization capability” in the literature), which is as-

sociated with flexible manufacturing systems. As stated earlier, it is critical for the

success of a mass customizer to be able to efficiently produce small batch sizes out of a

large range of product variants with short lead times; that is to be highly flexible and

efficient at the same time. Here, the literature suggests various activities or technologies

relating to such mass customization-specific manufacturing systems that together build

a manufacturing capability for mass customization.

However, not only mass customizers are forced to implement flexible but efficient

manufacturing systems. Industries that are characterized by high levels of dynamics in-

duced by shortening product life cycles or regionally differing customer requirements also

prompt non-customizers to increase their overall flexibility while maintaining efficiency

in manufacturing in order to cope with the increased levels of instability (ElMaraghy,

2005). This leads us to the first objective of this thesis. Can specific capabilities, e.g.,

mass customization manufacturing capability, help non-customization companies bet-

ter cope with the challenge of increasing flexibility while maintaining efficiency? Will

a non-customizer in a dynamic market environment benefit from implementing mass

customization manufacturing capability?

As it becomes evident from the literature review, specific capabilities are required to

successfully pursue a mass customization strategy. However, the available framework for

strategic mass customization capabilities does not provide a sufficient level of information

on the capabilities and their constituting elements, nor is there any empirical proof

for the framework. Besides the lack of a sound conceptualization of these required

capabilities, the literature review reveals that comprehensive and detailed measurement

scales for strategic mass customization capabilities are also still missing. The existing

scales for mass customization remain rather broad, which might be due to the reflective

nature of these scales. However, from the perspective of the resource-based view of

the firm, the capabilities for mass customization reflect a customizer’s ability to utilize

its resources, e.g., technologies or managerial practices, in a purposeful way. Thus, a

formative measurement approach seems to be more appropriate in this context, as it

uses a behavioral perspective on the research object.

Therefore, the second objective is to systematically review the literature on mass

18

customization in order to identify the activities and resources that form the foundation of

the required capabilities for mass customization, and to derive a sound conceptualization

of the capabilities. Based on this, it is worthwhile to develop a suitable measurement

approach that enables researchers and practitioners to evaluate and benchmark compa-

nies’ mass customization capabilities against each other. This motivates the following

research questions: What are the constituting elements, i.e., activities and resources, of

the capabilities for mass customization? What are proper definitions of the required ca-

pabilities for mass customization? How can a customizer’s capabilities be evaluated? Is a

formative measurement approach best-suited for measuring these strategic capabilities?

Besides the issues relating to the conceptualization of the capabilities for mass cus-

tomization and the discussion of their constituting elements, the existing literature also

fails to investigate relationships between these capabilities and their impact on com-

pany performance. As stated in the literature, the ability to develop a solution space

that meets customers’ heterogeneous needs in a market is positively related to company

performance. Moreover, it is also broadly accepted that solution space development ca-

pability alone is not sufficient to achieve success – companies also need a flexible and at

the same time efficient manufacturing system when pursuing a mass customization strat-

egy, as well as a choice navigation capability in order to be able to successfully market

customized products. Without doubt, these capabilities are connected with each other:

For example, when setting up a solution space, its complexity will affect operations and

manufacturing as well as the requirements of the customer interaction during the sales

processes. However, whether these capabilities are supplementary, complementary, or

related in another way has not been addressed by the existing research in a satisfactory

manner. Accordingly, the third objective of this thesis is to investigate the relations

between the specific capabilities for mass customization as well as their impact on com-

pany performance. In doing so, I rely on the results of the previous work in this thesis.

This leads us to the following research questions: How are the capabilities related to

each other? Do they complement or supplement each other, or is there another specific

order? How do they impact company performance?

The research questions derived above are addressed in this thesis by three research

papers that are presented briefly in the next chapter.

Summary of the research papers

The three papers of this dissertation attempt to answer the research questions identified

in the previous section. An overview of the research questions, the associated research

paper, and the chosen methodological approaches of each of the three papers as well as

their main findings is shown in Table 3.4. A brief summary of each paper is subsequently

presented at the end of this chapter.

Table 3.4: Overview of research questions, research papers, and methodological ap-proaches applied in this thesis

Research paper I Bridging strategic flexibility and operational efficiency:The role of operational capabilities

What we know: Dynamic market conditions require flexibility in organi-zational acting. However, flexibility deteriorate opera-tional efficiency and thus a company’s success.

What we don’t know(research question):

Are specific capabilities, e.g., mass customization manu-facturing capability, able to help non-customization com-panies better cope with the challenge of increasing flexi-bility while maintaining efficiency?

Goal of this paper: Investigation of the effect of the two operational capabil-ities on the link between strategic flexibility and opera-tional performance.

Methodologicalapproach:

• Literature research• Survey of 86 US manufacturing companies

Major result: Mass customization manufacturing capability mediatesthe relationship between strategic flexibility and opera-tional efficiency, thus, helps to mitigate the tradeoff be-tween the two variables.


19

20


Research paper II Measuring firms’ capabilities for mass customization:Construction of a formative measurement index

What we know: Pursuing a MC strategy successfully requires compa-nies to develop and implement specific organizationalcapabilities.


Notwithstanding the multiple papers dealing with capa-bilities for MC, clear definitions of these capabilities aswell as a description of their constituting elements arealso still missing. Furthermore, there is no comprehen-sive measurement approach available allowing to measurethese capabilities.

Goal of this paper: Conceptualization of the required capabilities for MC andtheir constituting elements. Moreover, a measurementinstrument for the three capabilities is developed.


• Literature research• 67 semi-structured expert interviews• Expert workshops• Online-based survey of 96 European B2B-companies

Major result: Definitions and conceptualizations of three capabilitiesfor MC. Repository of 71 managerial activities and orga-nizational resources that can serve as building blocks forthese MC capabilities. Moreover, a formative measure-ment index for each of the MC capability is developed.

Research paper III High-variety product offerings and company perfor-mance: The mediating roles of robust process design andinteraction competence

What we know: Companies must develop and implement specific MC ca-pabilities to be able to successfully pursue MC.


How are the MC capabilities related to each other andhow do they impact performance?

Goal of this paper: Investigation of the relations between the three capabil-ities for MC and their impact on company performanceand financial success.


• Literature research• Online-based survey of 192 B2B-companies• Objective (financial) data of the companies

Major result: Empirical evidence for the relevance and performance ef-fect of the three capabilities for MC (confirmation of theproposition of research paper II). Provision of a validatedorganizational configuration for MC companies consistingof the three strategic capabilities.

21

As Table 3.4 shows, a mixed-method approach was applied for this dissertation. This

approach relies on a combination of different complementary data collection approaches

such as qualitative and quantitative methods (Bryman, 2006). The mixed-method ap-

proach can thereby provide a more comprehensive picture of the research object com-

pared to stand-alone investigations based on a single methodological approach (Bryman,

2006). In this thesis, I combine reviews of different streams of literature with 67 expert

interviews and workshops and three quantitative analyses of company data which were

obtained from objective and subjective measures. Next, a summary of the three papers

is provided.

3.1 Research paper I: Bridging strategic flexibility and op-

erational efficiency: The role of operational capabili-

ties

In today’s ever-changing markets, which are characterized by regionally diverging cus-

tomer requirements and shortening product life cycles, companies are forced to increase

their strategic flexibility. This enables them to adapt and (re-)allocate organizational re-

sources more quickly, thereby allowing companies to remain competitive in such dynamic

environments. However, high levels of flexibility in organizational action are associated

with lower operational efficiency. Previous research states that companies need to bal-

ance both efficiency and flexibility in dynamic business environments; however, this is

difficult to achieve.

This study addresses this issue by investigating how the tradeoff between flexibility

and efficiency can be mitigated. Building on the resource-based view and its dynamic

extension it is suggested in the paper that specific operational capabilities might mediate

the direct negative association between flexibility and efficiency. This is in line with the

notion that in fast-changing business environments, dynamic capabilities are a necessary

precondition for success, but are not sufficient on their own. In the study, an integrative

framework is hypothesized including two operational capabilities referred to as mass cus-

tomization manufacturing capability, which describes a company’s ability to efficiently

and at the same time flexibly manufacture a high variety and customized products, and

technological capability, which evaluates a company’s ability to identify new technologies

22

and employ its technological resources to (re-)combine organizational components, e.g.,

links between processes, to be able to provide products with the desired characteristics.

In order to assess the suggested mediation model, quantitative data was analyzed

which was collected through an online-based survey among manufacturing companies in

the US. By employing a structural equation modeling approach as well as three mediation

tests, the mediating roles of mass customization manufacturing capability and techno-

logical capability on the relation between strategic flexibility and operational efficiency

was evaluated. However, the results do not provide support for all of the hypotheses.

The data analysis confirms that mass customization manufacturing capability acts as a

mediator, thus helping to reduce the tradeoff between flexibility and efficiency. This find-

ing is important for non-customizers who that are forced to increase their flexibility in

strategic action due to high levels of dynamics in their business environment. However,

while the data analysis confirms that mass customization manufacturing capability acts

as a mediator, the results do not support the mediating role of technological capability.

This suggests that future research should investigate the role of technology capability in

this context in more detail.

3.2 Research paper II: Measuring firms’ capabilities for

mass customization: Construction of a formative mea-

surement index2

In the literature on mass customization, there is ample conceptual and empirical work

emphasizing that companies need to develop and implement specific organizational ca-

pabilities when pursuing such a strategy. However, the existing research has neglected

to provide a comprehensive conceptualization of these capabilities and their underlying

managerial activities and organizational resources. Instead, there are only a few guide-

lines suggesting some managerial approaches or technologies that foster a company’s

mass customization manufacturing capability. This lack of more comprehensive and

concrete guidance may hinder the successful implementation and exploitation of mass

customization strategies, for which there are numerous examples. Furthermore, the aca-

demic literature does not yet provide an adequate measurement approach. Therefore,

2This research and paper has been prepared in cooperation with Frank Steiner and is also part ofMr. Steiner’s dissertation.

23

researchers as well as managers are unable to evaluate a company’s ability to efficiently

customize products on a large scale.

This paper attempts to address these shortcomings by developing formative mea-

surement indices for mass customization capabilities. Following a well-established guide-

line for the index development procedure proposed by Diamantopoulos and Winkl-

hofer (2001), the first step requires defining the scope of the respective latent construct

in a detailed way to ensure that the construct is understood in the broad sense. Using a

conceptual mass customization capabilities framework suggested by Salvador et al.

(2009) as a starting point, a literature review as well as expert interviews and workshops

were conducted in order to identify managerial activities and organizational resources

relating to the three mass customization capabilities in the framework. As the main

result of this step the originally proposed framework was extended by breaking each of

the three capabilities down into two subdimensions and providing definitions for each

of the capabilities and their subdimensions. Furthermore, 71 managerial activities and

organizational resources were identified which are each linked to one of the capabili-

ties. In the final step, the formative indices for the three capabilities were empirically

derived using data obtained from a questionnaire-based survey among 96 companies

pursuing a mass customization strategy. After an evaluation of the required reflective

constructs required to develop the formative indices and assess the formative indicator

collinearity, three formative-formative second-order constructs for the mass customiza-

tion capabilities and their subdimensions were formed. Finally, the external validity of

these second-order constructs was evaluated, resulting in the three final indices for the

capabilities.

The resulting indices represent a valuable contribution to the research field. They

allow researchers and managers to measure and evaluate a company’s ability to customize

products on large scale, and lay the foundation for benchmarking studies. Furthermore,

using the list of managerial activities and organizational resources enables companies to

identify complementary activities and resources that might help to improve their ability

to successfully pursue a mass customization strategy.

24

3.3 Research paper III: High-variety product offerings and

company performance: The mediating roles of robust

process design and interaction competence

The third paper is dedicated to the issues of how the three required organizational ca-

pabilities for high-variety production strategies, i.e., solution space development, robust

process design, and interaction competence (Salvador et al., 2009; Wellige & Steiner,

2014), are related to each other and to company performance. To determine this, I com-

bine insights from the literature on mass customization, high-variety production strate-

gies, and marketing with contingency theory of organizational design. In a first step,

I conceptualize an organizational configuration relating the three capabilities and com-

pany performance to each other. I suggest an organizational configuration that directly

links the solution space development capability with company performance. However,

I suggest that the effect of the solution space development capability on company per-

formance is not direct, but instead occurs through the two mediators: robust process

design capability, which is required for flexible and efficient manufacturing processes,

and the interaction competence capability, which enables efficient and effective informa-

tion processing and exchange. In order to validate this theoretical conceptualization,

I evaluate the model empirically using data obtained from a large-scale survey among

executives of manufacturing companies in Germany, Austria, and Switzerland.

I combine different analytical approaches to provide valid results. First, following

Baron and Kenny (1986), I apply a hierarchical regression approach to investigate

the mediating roles of robust process design and interaction competence on the link

between solution space development and company performance. This analysis provides

support for the hypothesized model. In order to account for the multiple mediation,

I further apply a bootstrapping approach which allows several mediators to be taken

into account at the same time and is regarded as the most appropriate approach for my

model (cf. Preacher & Hayes, 2008). The results of this analysis confirmed the findings

of the prior analysis that all three capabilities are relevant for achieving success. Thus,

besides supporting my hypotheses, the findings support the proposed organizational

configuration for mass customization strategies. Additionally, companies pursuing a

mass customization strategy should recognize that these findings suggest that decisions

on investments in RPD or IC should be made on the basis of the marginal benefits

25

associated with the investment. With this, I contribute to the literature in the field

by improving our understanding of the relevance of and relations between the three

capabilities for mass customization.

General discussion and conclusion

This research aims to improve our understanding of organizational capabilities for mass

customization, their relevance for companies, and their impact on company performance.

Each of my papers addresses a distinct aspect that contributes to these overarching goals

of the thesis. The results of the first paper suggest that mass customization manufac-

turing capability, i.e., an operational manufacturing capability, helps non-customizers to

mitigate the tradeoff between flexibility and operational efficiency. This paper remains

on a rather general level by investigating the role of flexible but efficient manufactur-

ing systems, which are one critical enabler of mass customization for non-customizers

acting in dynamic business environments. The second and third paper, in contrast, are

focused on companies pursuing a mass customization strategy by developing a formative

measurement approach that allows evaluation of the mass customization capabilities of

companies. Furthermore, the interrelations between these capabilities are conceptualized

and their effect on performance investigated.

Each of the three papers provides theoretical and managerial implications that are

discussed in the following. As with all empirical research, the papers are not free of

limitations and lay the foundation for future research, which will be discussed in this

chapter. Moreover, in the limitations section, I also reflect on the applied methodologies

and some general aspects of empirical research related to this dissertation. In doing so,

I critically discuss the advantages and disadvantages of some of the chosen analytical

approaches and methods and contrast them with alternative approaches that were not

selected; furthermore, I demonstrate my progression in empirical analysis during the

preparation of this thesis. Finally, this chapter closes with a brief conclusion.

27

28

4.1 Theoretical implications

The results of the research papers provide valuable insights for the academic literature

by improving our understanding of organizational capabilities for mass customization

and their interrelations and performance implications.

The findings of the investigations of this thesis contribute to the theory on the

RBV and its dynamic capabilities extension. By addressing the question of how non-

customization companies can mitigate the tradeoff between required strategic flexibility

and operational efficiency (Eisenhardt et al., 2010), I provide empirical evidence that a

mass customization capability in manufacturing helps to mitigate the conflict of objec-

tives between strategic flexibility and operational efficiency. This finding provides proof

for the frequently stated argument that dynamic capabilities on a strategic level, e.g.,

strategic flexibility, must be complemented by suitable capabilities on the operational

level (Eisenhardt & Martin, 2000; Teece, 2007; Helfat et al., 2009; Helfat & Winter,

2011). This also confirms recent research on mass customization which states that mass

customization manufacturing capability and comparable capabilities dedicated to the

implementation of flexible but efficient manufacturing systems help to increase opera-

tional efficiency when high levels of internal flexibility are required (Boyer, 1999; Tu

et al., 2001, 2004a; Huang et al., 2008). Moreover, the results suggest that develop-

ing and implementing flexibility on a strategic level enables non-customizers to flexibly

and quickly (re-)allocate organizational resources, which in turn fosters mass customiza-

tion manufacturing capability. Thus, strategic flexibility indirectly helps to increase

operational efficiency, i.e., time-based and cost-based performance, through mass cus-

tomization manufacturing capability.

Furthermore, the research papers of this dissertation contribute directly to the body

of research on mass customization and provide several theoretical implications. I provide

a comprehensive outline of different managerial activities and organizational resources

that together form the strategic capabilities for mass customization. Moreover, the sug-

gested systematic conceptualization of the six capability dimensions helps researchers to

better understand why some companies succeed and others fail in pursuing and imple-

menting mass customization. These insights might serve as a starting point for further

research and may help to extend the future scope of mass customization theory.

29

A further major contribution of this thesis are the formative measurement indices

for strategic capabilities that are required for mass customization strategies. With these

indices, I provide formative metrics which allow researchers to comprehensively compare

companies in terms of their ability to pursue a mass customization strategy. Future re-

search can rely on the presented indices when conducting survey-based empirical studies

in the field of mass customization and is enabled to conduct additional comprehensive

studies covering all capabilities simultaneously. This represents a significant contribution

to research methodology, as existing studies in the field use only reflective measurement

approaches for mass customization and are therefore unable to grasp all facets of the

respective capabilities in such detail.

In addition, most of the recent studies are focused only on single aspects or ca-

pabilities and thus do not provide insights on potential interrelationships between the

capabilities. By using the newly developed indices, the relations between the mass cus-

tomization capabilities and the performance effects could be investigated. I conducted

a multilevel analysis relying on a combination of perceptual data, obtained from a sur-

vey among managers, and financial data, and provide empirical evidence that all three

capabilities for mass customization in the B2B context are required at the same time.

I thereby contribute to the research on mass customization by investigating the per-

formance implications of the three capabilities in an industry context for the first time.

Taking both the different functional areas of companies pursuing mass customization and

the relationships between the specific capabilities related to these functional areas into

account provides a more holistic perspective on companies pursuing mass customization

than the previous research. Furthermore, this supports recent theoretical and concep-

tual work on mass customization capabilities (Salvador et al., 2009; Wellige & Steiner,

2014).

Additionally, I conceptually derived an organizational configuration for mass cus-

tomization based on the three strategic capabilities. I followed the suggestion of van

den Ven et al. (2013) by empirically analyzing theoretically derived organizational

configurations. The results support my configuration, which suggests that the three ca-

pabilities along the value chain are required to achieve performance. Furthermore, the

findings reveal that company performance positively affects financial success. Overall,

these investigations extend the research, as they not only examine the performance effect

30

of single capabilities but also take different specific capabilities required for mass cus-

tomization into account. From a methodological point of view, the conducted analyses

take multiple indirect effects into account, thus going beyond the existing research on

mass customization. Following the recommendation of Preacher and Hayes (2008),

I include multiple mediators in a single multiple mediation model in order to provide

more precise results and reduce the probability of parameter bias (Judd & Kenny, 1981).

In doing so, I suggest a theoretical foundation of mass customization in the organi-

zational configuration theory and provide an organizational configuration reconciling

environmental circumstances, strategy, and capabilities of different functional areas of

companies.

Overall, the three studies of this dissertation help researchers get a better theo-

retical understanding of underlying organizational configurational aspects and required

capabilities for mass customization. Furthermore, they extend our knowledge of the in-

terrelations between the required capabilities and their effects on company performance.

4.2 Managerial implications

The research of this dissertation also has some important implications for managers and

companies. First, I provide evidence of the relevance of a distinct mass customization

manufacturing capability for producing companies in dynamic business environments.

Second, I provide a comprehensive conceptualization of the strategic capabilities required

for mass customization as well as a measurement approach that allows mass customiza-

tion companies to be measured and benchmarked with regard to the required strategic

capabilities. Furthermore, it enables companies and managers to identify potential for

improving their business. Third and finally, I suggest an organizational configuration

for mass customization, thereby explaining the interdependencies among the required

capabilities for such strategies. These implications are discussed in more detail in the

following.

The results suggest that non-customization manufacturing companies that are con-

fronted with dynamic environmental conditions can benefit from implementing a mass

customization manufacturing capability, e.g., a distinct capability fostering flexible but

31

efficient manufacturing. Such companies are forced to achieve a strategic flexibility en-

abling them to (re-)allocate organizational resources more flexibly. However, increasing

flexibility also endangers efficiency on operational level, so these companies face a conflict

of objectives. I provide managers and companies with empirical evidence that imple-

menting a mass customization manufacturing capability helps to mitigate the tradeoff

between flexible strategic action and operational efficiency. This result suggests that

managers of such companies should be aware of the need to implement complementary

capabilities on operational level in order to avoid negative effects resulting from the

development and implementation of strategic flexibility.

Another major outcome of this dissertation is the conceptualization of the capabil-

ities as well as the six formative measurement indices for these strategic capabilities for

mass customization. Both results are based on an extensive list of 71 managerial ac-

tivities and organizational resources related to mass customization. The indices enable

practitioners to evaluate their business in terms of mass customization readiness and

benchmark their company against their competitors. Therefore, all companies taking

part in the benchmark must answer the indicators that constitute the indices. Us-

ing the normalized indicator weights obtained from the index development procedures,

managers will receive an index value for each of the capabilities that can be directly

compared with the values of other companies. Moreover, the formative measurement

approach can also be used as a controlling instrument for improvement measures, for

example, by comparing two points in time, e.g., a status quo with a status after the

implementation of a new managerial activity. Thus, by using the indices managers will

receive immediate feedback on the success of distinct improvement efforts.

As the measurement approach is based on a list of activities and resources, managers

are able to derive concrete suggestions for improvement in the event that a benchmark

indicates that a capability is weakly developed in their company. Therefore, the list of

activities and resources that together form the six indices provides a valid starting point

for the identification of additional activities or resources that might help companies

improve their ability to pursue a mass customization strategy. However, some of the

originally identified activities and resources are not included in the final indices, partly

due to the specific cross-industry sample used for the index development. Therefore, I

suggest that managers should also go back to the original list of 71 in order to identify

32

additional company-specific activities and resources that might support improvement

efforts.

However, I go beyond providing the conceptualization and development of forma-

tive measurement indices for the strategic capabilities by investigating the interrelations

between the three capability dimensions for mass customization. The findings of this in-

vestigation are highly relevant for practitioners, as they reveal that companies pursuing

a mass customization strategy need to develop and implement all three strategic capa-

bilities for mass customization in order to achieve company performance and financial

success. However, the results also suggest that companies and managers should take the

dependencies among the three capabilities into account. The theoretically derived orga-

nizational configuration consisting of the three capabilities proposed in the paper was

empirically confirmed. Therefore, practitioners should note that, as a required product

development capability for mass customization strategies, the solution space develop-

ment capability is crucial to be able to offer a number of product variants that satisfies

a broad range of customers with heterogeneous needs as precisely as possible. However,

the results indicate that solution space development capability alone is not sufficient – it

should be complemented by the robust process design and interaction competence capa-

bilities that translate the offered solution space into company performance and financial

success. Thus, managers should be aware of the organizational configuration and its

relevance for success when developing and implementing mass customization, or when

improving their company’s ability to pursue mass customization.

Moreover, the resulting findings from empirical analyses reveal that the relative

magnitude of the effects of robust process design and interaction competence cannot be

distinguished empirically. Therefore, when practitioners have to decide on improvement

measures relating to the two capabilities, they should decide based on the marginal

benefits associated with the investment. Furthermore, as the solution space development

capability is positively related to the robust process design capability and interaction

competence capability, it might be beneficial to foster the interconnection and exchange

between the three functional areas in which these capabilities are located. This may help

to improve the coordination or success of implementation and improvement measures.

33

4.3 Limitations, reflections, and future research

Although the findings of the three research papers in this dissertation help to improve

our understanding of organizational prerequisites for mass customization strategies as

well as their drivers of success, there are some limitations restricting their interpretabil-

ity and generalizability. In the following subsection, I discuss the limitations of this

thesis in terms of the data used in the empirical analyses in this dissertation, the media-

tion analysis representing the core model in this thesis, and the formative measurement

approach, which has been frequently criticized by different researchers. Hereby, I ad-

dress three critical areas of empirical research: data basis, methodologies and analytical

approaches, and measurement approaches. In this context, I also critically reflect on

the chosen methodological approaches and thereby demonstrate the progression of my

empirical research abilities. Finally, I suggest potential directions for future research.

4.3.1 Data, sample size, and common method bias

A first limitation is related to the sample that was used to investigate the mediating

role of mass customization capability on the relation between strategic flexibility and

operational efficiency. The data was obtained from a survey conducted by a team of

researcher from the University of San Diego in the context of a larger research project

on organizational capabilities in dynamic market. However, the data used does not pro-

vide any information about the level of environmental dynamics faced by the companies

included in the survey. Nor do I have any information about whether a selection mech-

anism was applied when selecting the companies for the survey. Although I obtained

data about the industry affiliation of the companies in the sample, which would at least

allow for approximate controlling for industry-specific dynamics, I was not able to in-

tegrate this variable, as the sample size is too small. An integration of these variables

would dramatically reduce the explanatory power of the empirical analysis. I could only

assume that the industries represented in the sample are characterized by high levels of

dynamics; however, there is no indication for this. Therefore, the findings are limited

and should be interpreted with caution.

However, there is another and more general concern regarding this study that re-

lates to the sample size and chosen analytical methodology, which is discussed in the

34

following. It is frequently argued that partial least squares (PLS) approaches are suit-

able for the analyse of even the smallest sample sizes (Chin et al., 2003; Peng & Lai,

2012). The ten-times (sometimes even five-times) rule is an often cited guideline which

suggests that the minimum sample size needs to be at least ten times (five times) as

large as the number of path relations directed to the most complex construct in a given

model (Hair et al., 2012). I followed this suggestion when analyzing the mediating role

of mass customization capability on the relation between strategic flexibility and opera-

tional efficiency. However, this ten-times rule is not without criticism. The results of a

Monte Carlo simulation study conducted by Goodhue et al. (2006) suggest that using

bootstrapping in order to obtain p-values in PLS is not superior to other methods, as

it also lacks the adequate statistical power to detect small or even medium-sized effects

in small sample sizes. According to these results, the ten-times rule is no longer viable.

Overall, the contradictory guidance provided in the literature suggests that results ob-

tained from PLS estimates using small samples should be considered with caution. In

light of this, I attempted to get larger sample sizes in the other studies of this thesis in

order to avoid the difficulties discussed above.

In the following, I focus in detail on limitations that can be associated with the kind

of data used in this thesis. In this thesis. All (papers I and II) or most of (paper III) the

data used for analyses in this thesis are cross-sectional and based on perceptions of single

informants per company (self-reported data). Although this is consistent with common

practice in research (Huang et al., 2010; Lisboa et al., 2011), as it is frequently difficult

to acquire objective data and collect company-specific data over time, these data provide

only a snapshot of a company’s status quo, thereby limiting the ability to understand

causality between the factors. Moreover, another potential downside of using only self-

reported data, such as in research paper I, is that common method variance can bias

the results of the empirical analyses. Common method bias is defined as the variance

induced by the applied methodological measurement approach and survey procedure

rather than by the objective variance of constructs. It may occur when the values of

both the independent and the dependent variable are obtained from the same survey

respondent, as in the case of my first research paper3. Moreover, common method

variance can result from the item characteristics as well as the context in which an item

is placed (Podsakoff et al., 2003). Different studies reveal that common method bias

3As research paper II is aimed at developing measurement approach and the empirical analysis ofresearch paper III relies on objective financial data, common method bias is rather not an issue here

35

represents a widespread problem. For example, a meta-study by Cote and Buckley

(1987) finds that approximately one quarter of the variance in research measures that

has been investigated is caused by measurement error such as common method bias. The

size and direction of the effect caused by method variance can differ, and thus it can

cause Type I as well as Type II errors (Podsakoff et al., 2003). Although the strength

of a present effect of common method variance varies between studies, on average their

effect is significant (Podsakoff et al., 2003). Therefore, it is broadly accepted in the

scientific literature that researchers need to test whether common method variance is an

issue in survey-based empirical studies. Different methods are provided in the literature

that allow testing for common method variance. The single-factor test suggested by

Harman (cf. Harman, 1967; Podsakoff & Organ, 1986) is one of the most frequently

used test procedures (e.g. Atuahene-Gima et al., 2005; Zhang et al., 2009; Liu et al.,

2012) and has the underlying hypothesis that common method variance is an issue in

a study if most of its variables load on a single factor when conducting an exploratory

factor analysis. Therefore, I followed common practice by using this test procedure in

research paper I.

However, the single-factor test is not without criticism (Podsakoff et al., 2003): (1)

It does not provide statistical evidence of the absence of common method bias, (2) it

remains unclear how much of the variance between the items needs to be accounted

for the major factor, and (3) depending on the number of items in a study, the num-

ber of extracted factors likely increases as well, thereby reducing the validity of the test.

Podsakoff et al. (2003) therefore believe that the single-factor test is not a valid eva-

luation instrument for detecting common method variance, although it is still frequently

applied, and that it only represents a test procedure that indicates whether the common

method variance might be an issue. In order to obtain more valid assessments with

regard to the effect of common method variance, more advanced methods are suggested,

for example, partial correlation techniques (e.g. Lindell & Whitney, 2001).

According to the arguments presented above, it can be concluded that the results

obtained for the chosen test procedure in the first research paper should only be regarded

as a first indication that common method variance is likely not a problem, rather than

as a sufficient criterion to rule out the presence of such a bias in my study. It would

be beneficial for future research to rely on objective data or longitudinal data in order

to address potential shortcomings associated with the use of self-reported data. Using

36

longitudinal data allows detecting and controlling for time-induced changes in the value

of the dependent variable (Hsiao, 2003). Additionally, as the sample is larger, the use

of longitudinal data reduces the threat of collinearity among the independent variables

(Hsiao, 2003). Thus, I also suggest replicating at least the first studies of this thesis

using longitudinal data to gain a more valid understanding of the relationships in an

empirical model.

To prevent common method bias and to increase the validity of the model, I used

subjective and objective data to measure company success in the third research paper.

However, as the financial data were extracted from the last available annual report of

a company, within the sample ranging from 2011-13, there is a time offset between the

survey data and the objective financial data, which limits the explanatory power of the

results. Although it seems likely that the conditions of the surveyed companies will not

have changed essentially across the relatively short time offset, there is no guarantee for

this. I suggest repeating the analysis as soon as all financial data of the companies for

2013 are available.

4.3.2 Mediation analysis

The mediation analysis represents one of my core analytical methods applied in this

thesis. To test for mediation effects, I applied different approaches in research paper I

and III, which also demonstrates my journey and progress in empirical analysis in the

course of preparing this thesis. I will therefore discuss in more detail the mediation

analysis and why different approaches were selected in the research papers.

A mediator can be considered as a variable that partly or entirely carries an effect of

a given independent variable to a given dependent variable (Baron & Kenny, 1986; Sobel,

1990). In doing so, the independent variable significantly affects the dependent variable

in the absence of the mediator. When additionally taking the mediator into account,

the effect of the independent variable on the dependent variable declines or disappears

completely, while the independent variable significantly affects the mediator, which in

turn significantly affects the dependent variable. Different analytical approaches are

available, allowing testing for mediation.

37

A causal multi-step procedure suggested by Baron and Kenny (1986) allows to

informally test the mentioned series of hypothesis to evaluate whether or not a variable is

mediating a relationship between an independent and a dependent variable by using OLS

regressions or structural equation modeling. Although this procedure is most commonly

used for testing mediation, it does not provide statistical evidence of the mediated effect

and is thus limited in its predictive power to detect real effects particularly for small

sample sizes (MacKinnon et al., 2002; Preacher & Hayes, 2004).

This shortcoming is frequently addressed by using a set of statistical methods based

on the ratio of the product of the coefficients of the indirect path to the estimated

standard error, which is in contrast to the multi-step procedure focusing the single

paths in a mediation model (Preacher & Hayes, 2008). The most popular product-of-

coefficient method was proposed by Sobel (1982). It allows testing whether the indirect

effect differs significantly from zero, i.e., the paths from the independent variable to the

dependent variable through the mediator. Since the product-of-coefficient method is

well established and is frequently applied in recent research – even with small sample

sizes – (e.g. Bhagavatula et al., 2010; Lee et al., 2011) I rely on this method in the first

research paper of this dissertation in order to statistically investigate mediating effects.

However, product-of-coefficient methodological approaches have some limitations

that should be taken into account. As these approaches are of a parametric nature, a

normal distribution of the indirect effect is assumed for the calculation of its p-value,

which is oftentimes questionable, at least in small samples such as that in research

paper I. Therefore, it is recommended to use product-of-coefficient mediation tests, such

as the Sobel’s test, only when the sample is large, when the effects are large, and/or

in the event that raw data are not available (Preacher & Hayes, 2008). Although it is

difficult to estimate the impact of a skewed, non-normally distributed indirect effect on

the results, it reduces the predictive power of studies and thus the results of research

paper I. Besides this, in the first research paper, I tested for the mediating effect of

two mediators in two separate product-of-coefficient calculations. I used the values of

the coefficients and standard errors obtained from a structural equation modeling which

included both mediators at the same time. Thus, the effect of the other mediator is

indirectly considered in each of the product-of-coefficient calculations. However, by

using this approach, I was unable to evaluate several additional aspects (cf. Preacher &

Hayes, 2008): First, the chosen methodological approach did not allow us to calculate the

38

total indirect effect in order to investigate whether a joint mediating effect caused by all

expected mediators exists. Second, I am unable to provide evidence for the significance

and relative size of the specific indirect effect of each of the mediators when other

mediators are included in the model at the same time. Finally, it is more likely that

I have obtained biased parameter estimates, as I rely on the single-mediator models

which exclude the effects of other mediators, and thus potential effects between the

mediators (Judd & Kenny, 1981). Overall, it must be emphasized that, although the

chosen analytical approach in paper I is commonly used for mediation analyses in recent

studies, it has some limitations, i.e., it does not investigate relevant issues related to

models with multiple mediators.

Driven by theory, I also ended up with a multiple mediation model in the third

paper of this dissertation. In order to address some of the limitations associated with

the approach chosen in the first paper, I looked for a more appropriate approach. In

the context of mediation models with multiple mediators, a bootstrapping method is

thought to have advantages over the causal-multi-step procedure as well as the product-

of-coefficients approach (e.g. Bollen & Stine, 1990; MacKinnon et al., 2004; Preacher &

Hayes, 2004, 2008): First, as a nonparametric re-sampling procedure, bootstrapping does

not require normality of the sampling distribution of indirect effects; it can be applied

even when a given sample is small. Second, bootstrapping procedures allow mediation

models with multiple mediators to be estimated. These procedures thereby allow an

investigation of the effect and significance of each single mediator in the presence of the

others, the differences between two mediators in terms of their relevance, and the total

indirect effect of the given set of mediators. Finally, the abovementioned problem with

biased parameter estimates is reduced. Overall, the bootstrapping method used in paper

III provides more comprehensive and valid results compared to the approach chosen in

paper I.

4.3.3 Formative measurement approach

Unlike most empirical studies in management research, two studies of this thesis rely

on the formative measurement approach. As several researchers have raised concerns

with regard to the development and use of formative measurement indices in empirical

research (Borsboom et al., 2003; Howell et al., 2007b; Wilcox et al., 2008; Edwards, 2011),

39

I set out in detail the reasons for choosing this approach and discuss the advantages and

disadvantages of the formative approach in the following.

Although the formative approach gets a lot of criticism (Borsboom et al., 2003;

Howell et al., 2007a,b; Wilcox et al., 2008; Edwards, 2011), this approach was chosen as

it is expected to better suit the purposes of this dissertation than the reflective approach.

Unlike reflective measurement, the formative approach regards latent constructs from a

behavioral perspective (Coltman et al., 2008) as a multidimensional construct consisting

of the sum of all independent activities that cause a variation in the construct of interest

(Bollen, 1989; Diamantopoulos & Siguaw, 2006). In order to gain the required deep

understanding of the construct of interest, the development procedure for a formative

index is associated with a comprehensive investigation and analysis of this construct

(Diamantopoulos & Winklhofer, 2001). This helps to increase the understanding of the

focal construct, as researchers are forced to comprehensively analyze its scope on a level

of its constituent parts, so that all aspects and potential interrelations are adequately

considered (Diamantopoulos & Winklhofer, 2001).

The identification of formative measurement models is associated with some chal-

lenges. In order to achieve an identification of formative measurement models, two

or more reflective items have to be integrated into the model (MacCallum & Browne,

1993). Thus, a pure formative measurement model is not identified, which results in

a latent construct that is uninterpretable (Bollen & Lennox, 1991). Including at least

two external, reflective criteria is problematic for two reasons (Edwards, 2011): First,

the reflective measures have to reflect the entire scope of the latent construct with the

inherent risk that items become ambiguous. Second, the integrated reflective items will

affect the meaning of the latent construct itself (Howell et al., 2007a). Thus, depending

on the chosen reflective items, the meaning of the latent construct will change, and with

it the inferences based on it (Edwards, 2011). This limits the benefits of using formative

indices in the context of structural equation models.

Another point of criticism is the assumption that indicators can be measured free of

any measurement error (Diamantopoulos & Siguaw, 2006). This is difficult to defend, as

common data collection methods such as interviews, self-reports, and surveys are prone

to error to some degree, for example, due to different interpretations of the indicators by

different participants in a survey (Edwards, 2011). This might lead to biased estimates

40

of the indicator loadings (Edwards, 2011) and thus to a reduction of the explanatory

power of the construct.

Some authors raise concerns regarding how latent constructs, indicators, and causal-

ity are oftentimes conceptualized (Borsboom, 2005; Howell et al., 2007a). According to

these authors, the overlap of a latent construct with its indicators as well as the mea-

sures by themselves are equated with facets that possess causal potency, whereby errors

that exist almost without exception are neglected. However, as this is more an opinion

on causality underlying formative measurement than a fact (Edwards, 2011), it is the

responsibility of researchers to compare the nominal definition of a formative latent con-

struct with its empirical realization during the development of a new formative index

and, if necessary, to change the discussion of the construct (Howell et al., 2007b).

Moreover, there are concerns in terms of dimensionality. Formative indices repre-

sent multidimensional constructs consisting of indicators which preferably do not overlap

(Diamantopoulos & Winklhofer, 2001; Diamantopoulos, 2006). Therefore, removing or

neglecting one indicator will remove a part of the construct and change its meaning

(Bollen & Lennox, 1991; MacKenzie et al., 2005). As the conceptually heterogeneous

indicators of a formative construct need not necessarily have the same antecedents and

consequences or the same nomological network (Howell et al., 2007b), combining them

into a single variable will result in a conceptually ambiguous formative construct, which

gives rise to the question of whether it can be meaningfully be interpreted (Edwards,

2011). Although researchers can partly resolve the conceptual ambiguity of latent for-

mative constructs, Edwards (2011, p. 374) states that ”the multidimensionality of

formative measures should be considered a liability, not a property that formative mea-

surement models accommodate in some useful fashion.”

However, although the concerns over the formative measurement approach are rea-

sonable and valid, most of the abovementioned concerns can be addressed by carefully

taking them into account during the development of a new formative index, as I did in

this thesis. The development procedure, especially the used reflective items for identi-

fication, should also be carefully taken into account when using indices. However, even

some of the critics regard formative measurement as a valid methodological approach,

especially for benchmark studies or for tracking the success of the development and im-

plementation of business strategies (Arnett et al., 2003; Howell et al., 2007b) and the

41

analysis of latent constructs from a behavioral perspective (Coltman et al., 2008), which

represents the objective of my study.

Overall, the formative approach is seen as suitable for the purposes of this disserta-

tion, which proposes that mass customization capabilities are sums of distinct manage-

rial activities and organizational resources. The formative index development procedure

also contributes to deepening our knowledge of the required capabilities for mass cus-

tomization as well as their constituting managerial activities and organizational routines.

Furthermore, the formative index approach enables mass customizers to evaluate and

benchmark the current state of their capabilities and to detect shortcomings. More-

over, based on the extensive list of activities and procedures that underlie a formative

construct, the formative indices allow companies to identify potential to improve their

capabilities. In conclusion, despite the criticisms, for the purposes of this thesis, the

formative measurement approach is regarded as a suitable and valid methodological

approach for benchmarking and performance studies when used carefully. Although I

regard the developed formative indices as valid instruments for mass customization ca-

pabilities, a replication of the results using another set of data is required in order to

cross-validate the instruments (Cudeck & Browne, 1983; Diamantopoulos & Winklhofer,

2001).

4.3.4 Future research

The concerns and limitations of the three research papers outlined above suggest po-

tential directions for further research. In the following, I will exemplarily discuss some

directions that are deemed particularly relevant.

In order to address the shortcomings associated with the small sample size in the

first paper, I suggest that future research should replicate this study by using a sample

of companies which are verifiably confronted with dynamic environments. Preferably

researcher thereby rely on a larger (longitudinal) data set and/or apply another ana-

lytical approach. Additionally, it would be beneficial to control for different dimensions

of environmental dynamics and change, such as pace, predictability, complexity, and

ambiguity (Davis et al., 2009).

42

During the investigations of the mediating role of operational capabilities on the

conflict of objectives between strategic flexibility and operational efficiency, the empir-

ical analysis did not provide evidence of a mediating effect of technological capability

on the relation between strategic flexibility and operational efficiency. I suggest that

future research should investigate the relations between the constructs in the model in

more detail. It might be worthwhile to investigate whether moderators influence these

relations, which is not researched in this paper. Besides the potential influence of moder-

ators, further mediating variables which suppresses the mediating effect of technological

capability might also be relevant in this context. These might be further capabilities

on other organizational levels that also influence the tradeoff between flexibility and

efficiency, or uncovered relationships between the variables in the model. Overall, only a

very limited number of factors are taken into account; the research model is thus by no

means complete. Future research should therefore include additional factors from other

levels as well as further controls in the analysis, or the abovementioned control measures

for environmental dynamics.

During the index development procedure, not all of the 71 originally identified

activities and resources were included in the final formative indices, which may evolve

from the cross-sectional sample that was used. Therefore, it would be worthwhile to

investigate whether any industry-specific differences consist in terms of the relevance of

activities and routines. Furthermore, it might also beneficial to revise the indices over

time in order to ensure that the formative indices remain valid measurement instruments

for mass customization capabilities, as it is expectable that new management activities

and organizational resources evolve over time.

The existing literature does not provide guidance on how companies should develop

and implement the mass customization capabilities simultaneously. I suggest that future

research should further examine the evolution of the development and implementation

process for the capabilities and provide best-practices cases. In doing so, following the

suggestion of Gavetti (2005), future studies should especially consider the microfoun-

dations of the capability development process and the influence of managers’ cognition

and hierarchy, which has been largely neglected in the field of organizational research.

Moreover, recent research on mass customization indicates that the organizational struc-

ture affects a company’s ability to develop a manufacturing system capable of efficiently

producing customized products on a large scale (Huang et al., 2010). This seems to be

43

relevant and should therefore be integrated into future research on the development and

implementation of the three capabilities.

I proposed an organizational configuration for mass customization companies and

could provide empirical evidence for their relevance. However, although literature on

configuration theory suggests that only one or a few optimal configurations for a distinct

strategic goal might exist (Vorhies & Morgan, 2003; Wiklund & Shepherd, 2005), I do

not provide evidence that the proposed configuration for mass customization is ideal for

achieving highest performance. Therefore, the proposed configuration should rather be

seen as one promising configuration. To add to this discussion, it seems to be worthwhile

that future research compares various existing configurations in the context of mass

customization which also take additional organizational variables into account (van de

Ven et al., 2013).

4.4 Conclusion

A condensed overview of the main research objectives and the associated major results of

this dissertation shall close this part I (see Table 4.5). The overall objective of this thesis

is to improve our understanding of specific capabilities for mass customization, their ef-

fects on company performance, and their mutual influence. By conducting three studies,

I provide well-founded conceptualizations and definitions of mass customization capabili-

ties as well as a valid measurement approach. Furthermore, I provide empirical evidence

for the relevance and relations between these mass customization capabilities, which

is especially relevant for practitioners when implementing a mass customization strat-

egy or improving a company’s ability to pursue such a strategy. Finally, I have shown

that even non-customizers who are confronted with dynamic business environments can

benefit from implementing mass customization principles, i.e., mass customization man-

ufacturing capability. These main results of the thesis contribute substantially to the

literature in the field of mass customization, but also to the literature on operations

management or organization science.

44

Table 4.5: Overview of main research objectives and results of this thesis

Research objectives Major findings

Investigation of the abilityof specific capabilities, e.g.mass customization manufac-turing capability, to help non-customizers to better copewith the challenge of increas-ing flexibility while maintain-ing efficiency.

Main result: Mass customization manufactur-ing capability helps to reduce the trade-off betweenflexibility in strategic acting and operational efficiency.

Further contributions and relevance:

• Contribution to RBV and dynamic capability the-ory: confirming the relevance of distinct capabilitieson strategic and operational level

• Strategic flexibility increases a company’s masscustomization manufacturing capability

Provision of proper definitionsfor the required capabilities formass customization as well asdescription of their constitut-ing elements. Development ofa measurement approach forthe mass customization capa-bilities.

Main results: Well-founded conceptualization ofthe required capabilities for mass customization anddescription of their constituting elements. Provisionof formative measurement indices that enables toevaluate and benchmark customizers in terms of theirmass customization capabilities.


• Repository of 71 managerial activities and organi-zational resources that serve as building blocks forthe mass customization capabilities

• Formal anchoring of the mass customizationcapabilities perspective in the RBV

Investigation and descriptionof the relations between themass customization capabili-ties and their impact on com-pany performance.

Main result: The relationship between a company’sability to develop a product offering of high marketrelevance (SSD; early stage of the value chain) andcompany performance is mediated by the mass cus-tomization capabilities for RPD and IC (late stagesof the value chain).


• Provision of a validated organizational configurationfor MC companies consisting of the three strategiccapabilities

• Confirmation of previous research propositions: allthree capabilities are relevant

• Investment decisions between RPD and IC shouldbe based on marginal benefits, as both are equallyrelevant

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Part II

Research Papers

63

Paper 1

Bridging strategic flexibility and

operational efficiency: The role of

operational capabilities

Abstract: It is suggested in literature that companies that are confronted with dy-

namic business environments need to implement a strategic flexibility that enables them

to flexibly (re-) allocate their organizational resources. However, flexibility is also associ-

ated with decreasing operational efficiency. We analyze this tradeoff by investigating the

mediating role of operational capabilities, i.e. mass customization capability and techno-

logical capability, that suggest mitigating this negative relationship between flexibility and

efficiency. Building on strategic and operations management literature, we derive hy-

potheses and test them using data from a sample of 86 US manufacturing companies. The

results suggest that the mass customization capability mediates the relationship between

strategic flexibility and operational efficiency, and thus, helps to mitigate the tradeoff

between flexibility on strategic level and efficiency in operations. However, the findings

do not provide empirical evidence for the mediating role of the technological capability.

We discuss these findings which are of high relevance for academics and practitioners in

the field of strategic management and operations management.

Keywords: Strategic flexibility, operational efficiency, mass customization;

technological capability.

65

66

Status:

• Working paper.

Presented at:

• TIM doctoral seminar, June 2012, RWTH Aachen University.

67

Introduction

Today’s markets are often shaped by frequent changes caused by globalization (Wiggins

& Ruefli, 2005), increasingly heterogeneous customer needs (Tu et al., 2004b), growth

in research and innovation activities, and shortened product life cycles (Droge et al.,

2008). In such dynamic markets, companies need a strategic flexibility that fosters flexi-

ble adaption and reallocation of organizational resources (Nadkarni & Narayanan, 2007),

which helps to maintain competitiveness (Schreyogg & Sydow, 2010). However, imple-

menting flexibility is associated with the risk of losing efficiency in operations (Adler

et al., 1999; Baker & Nelson, 2005). Operational efficiency, as a necessary precondition

for sustained competitive advantage (Krause et al., 2013), is associated with high degrees

of standardization, stability, and alignment in routines and processes in order to achieve

economies of scale (Ebben & Johnson, 2005). Here, the fundamental need emerges to

balance strategic flexibility and operational efficiency to maintain or build a competitive

advantage (Adler et al., 1999; Eisenhardt et al., 2010). Hence, a combination of strate-

gic flexibility and operational efficiency is difficult to achieve (e.g. Adler et al., 1999;

Ebben & Johnson, 2005; Eisenhardt et al., 2010). Whereas research provides evidence

for the relevance of strategic flexibility and operational efficiency, far less attention has

been given to the issue how companies can mitigate the tradeoff between these two op-

posing objectives. Hence, scholars have called for further examination and implications

for decision-makers of a suitable setup of capabilities to address the efficiency-flexibility

trade-off (e.g. Adler et al., 1999).

The objective of this study is to suggest an integrative framework describing how

strategic flexibility and operational efficiency can be combined. Thereby, we rest upon

the dynamic capabilities framework which is based on the resource-based view (RBV)

of a company. Dynamic capabilities relate to organizational and technological skills and

processes that are essential for the flexible (re-)allocation and concurrent exploitation of

resources to operate successfully in dynamic markets (e.g. Teece et al., 1997; Zhou & Wu,

2010). Strategic flexibility, i.e. the ability of a company to reallocate and reconfigure

its resources, processes and strategies, is regarded as one type of a dynamic capability

(Zhou & Wu, 2010). However, literature suggests that not only dynamic capabilities

on a strategic level are necessary, but also suiting capabilities on the operational level,

which effectively and efficiently perform their functions (Eisenhardt & Martin, 2000;

68

Teece, 2007; Helfat et al., 2009; Helfat & Winter, 2011). Therefore, capabilities on

different levels are distinguished (Collis, 1994; Winter, 2003; Helfat et al., 2009; Danneels,

2008). Winter (2003) considered dynamic capabilities as higher-level capabilities on

the strategic level that are required to orchestrate, change, and build lower-level or

operational capabilities. In contrast, capabilities on the operational level are essential to

perform tasks related to development, manufacturing, and delivery of products (Kaplan

& Norton, 2008).

In this line, we suggest that specific operational capabilities are needed to bridge the

trade-off between strategic flexibility and operational efficiency. Specifically, we propose

an integrative framework including a mass customization capability and a technological

capability as key mediating variables for the relationship between strategic flexibility and

operational efficiency. The mass customization capability enables companies to produce

high variety on large scale with short lead times and at close to mass-production costs

(Boyer, 1999; Tu et al., 2001, 2004a; Huang et al., 2008). By combining highly flexible

manufacturing processes with the efficient provision of diverse products, this operational

capability might help to balance the tradeoff between strategic flexibility and operational

efficiency. The technological capability is defined as a company’s ability to employ

its technological resources (e.g. patents, knowledge, machinery, etc.) to combine and

recombine components and linkages between the components, methods, processes and

techniques to be able to provide products with desired characteristics (Afuah, 2002).

Furthermore, it refers to mastering state-of-art technologies as well as the identification

of new technologies and response to technological changes (Zhou & Wu, 2010). Thus, it

is related to the continuous and flexible adaption of the technological base to create and

improve products and processes more quickly, thus, in turn also might help to increase

operational efficiency.

We research the above mentioned gap by using survey data from top-level man-

agers of US manufacturing companies to analyze how mass customization capability

and technological capability mediate the relationship between strategic flexibility and

operational efficiency. Thereby this study contributes to the literature on operations

management, strategic management in general, and mass customization strategies par-

ticularly in various ways. First, resting upon the RBV and the dynamic capability

theory, this study explicates how the need for a strategic flexibility in dynamic environ-

ments can be combined with the need for operational efficiency. More specifically, we

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provide insights into how the two operational capabilities, i.e. mass customization capa-

bility and technological capability, contribute to resolve the tradeoff between strategic

flexibility and operational efficiency. Second, we extend RBV and dynamic capabilities

literature through providing a more fine-grained perspective on how various capabili-

ties interact in the pursuit of sustainable competitive advantage. Finally, we provide

implications for theory and management related to balancing flexibility and efficiency.

We organize this paper as follows: First, we summarize the relevant literature on

strategic flexibility, operational efficiency and the two capabilities for MC and technol-

ogy. Then we analyze interdependencies and deduce hypotheses related to the relations

between the constructs. Thereafter, the research method is presented, followed by the

data analysis and a discussion of our findings. Finally, we present the implications of

the study and conclude with limitations and suggestions for further research.

Theoretical background and hypotheses

Strategic flexibility and operational efficiency

In the strategic management literature, the RBV is one approach to explain why some

companies perform better than others in a given industry. The RBV argues that com-

pany performance and success is based on a company’s portfolio of resources and capa-

bilities (Hoopes et al., 2003). Resources are tangible and intangible factors a company

possesses, whereas capabilities reflect the ability to use, combine, and exploit these re-

sources (Amit & Schoemaker, 1993). However, to explain company performance and

success in dynamic markets where a higher level of flexibility is needed, RBV comes to

its explanatory limits. Therefore, the framework was extended by the dynamic capa-

bilities approach (Teece et al., 1997; Eisenhardt & Martin, 2000; Winter, 2003; Teece,

2007). Dynamic capabilities refer to a company’s ability to effectively integrate, build,

reconfigure, (re-)align, and deploy its intangible and tangible assets to be able to con-

stantly redesign and invent itself to adjust flexibly to changing business environments

(Eisenhardt & Martin, 2000; Teece, 2007; Schreyogg & Sydow, 2010). Generally, these

capabilities relate more to the strategic level (Teece, 2007), and orchestrate operational

resources and capabilities that directly generate returns (Zott, 2003). Thereby, dynamic

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capabilities help sustaining and amplifying long term vitality and competitiveness of

companies (Zahra & George, 2002; Helfat et al., 2009).

Strategic flexibility is frequently considered as one important dynamic capability

that enables to obtain superior performance and success in dynamic industries, partic-

ularly in technological driven markets where products and processes are continuously

changing (e.g. Evans, 1991; Sanchez, 1995; Kotha, 1995; Eisenhardt & Martin, 2000; Gre-

wal & Tansuhaj, 2001; Johnson et al., 2003; Nadkarni & Narayanan, 2007; Eisenhardt

et al., 2010; Zhou & Wu, 2010). It is associated with a company’s ability to reposition

itself in a market (Harrigan, 1985), to respond more successfully to unforeseen environ-

mental changes (Eppink, 1978), and to quickly modify strategies to be able to leverage

existing competencies more effectively (Evans, 1991; Sanchez, 1995). Another study

suggests that strategic flexibility is necessary to flexibly coordinate the interdependent

resources in a company’s value chain (Sanchez, 1997). Thus, strategic flexibility helps

companies to remain competitive in dynamic environments by increasing the ability to

flexibly reallocate and reconfigure its resources, processes, and strategies (Zhou & Wu,

2010). Grewal and Tansuhaj (2001) provide evidence that company performance

in highly competitive environments is positively influenced by companies’ flexibility in

strategic decision making.

Whereas strategic flexibility is relevant on the strategic level, literature also suggests

that efficiency on the operational level is imperative for companies to remain its com-

petitiveness (Baker & Nelson, 2005; Ebben & Johnson, 2005; Eisenhardt et al., 2010).

Efficiency in operations is typically associated with formalized processes, highly stan-

dardized manufacturing processes directed to large production volumes of one or a few

standardized products at low costs per unit, i.e. economies of scales, in constant and

predictable quality (e.g. Adler et al., 1999; Ebben & Johnson, 2005; Eisenhardt et al.,

2010; Schreyogg & Sydow, 2010). To reach operational excellence, companies focus on

continuously improving and optimizing existing activities and processes (Vickery et al.,

1997). The emphasis is to reduce operational costs and cycle-times while maintain-

ing quality and customer satisfaction. Toyota is a well-known example for a company

that has reached an extraordinary competitive position by continuously improving op-

erational efficiency of the manufacturing system (Lander & Liker, 2007). Operational

excellence can be distinguished in a time and cost dimension. Both, time-based and

cost-based performance indicate to which extent a specific task is performed relatively

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to a reference point. The overall objective of time-based performance is to reduce the

required time in operations such as the manufacturing lead time or deliver time (Azzone

et al., 1991). Time-based performance is one critical factor for manufacturing companies

operating in dynamic markets (Kumar & Motwani, 1995). Cost efficiency, as the aim of

cost-based performance, is defined as the ability to execute operational tasks using as

little resources as possible (Swink et al., 2005).

As argued above, achieving flexibility in strategic and operational acting is critical

for the competitiveness of companies in dynamic environments. However, remaining

efficient in operations is also critical for their performance and long-term success. Thus,

companies need to master the challenge of harmonizing this trade-off between flexibility

and efficiency (Baker & Nelson, 2005; Ebben & Johnson, 2005; Eisenhardt et al., 2010).

Ebben and Johnson (2005) compared companies pursuing an efficiency or flexibility

strategy with companies that pursue both at same time. They provide evidence that

companies pursuing a hybrid strategy show lower overall performance. In another study,

Baker and Nelson (2005) state that high levels of flexibility increase the probability

of losing efficiency. Overall, these studies confirm the existence of serious difficulties for

companies to combine flexibility and efficiency.

Based on these insights, we highlight the important role of the operational level

capabilities. We suggest that two specific operational capabilities referred to as mass

customization capability and technological capability help mitigating the tradeoff be-

tween strategic flexibility in operational efficiency (see Figure 1.1). This is discussed in

the following sections in a detailed way.

Figure 1.1: Hypothesized model

.

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Strategic flexibility, mass customization capability, and operational ef-

ficiency

We propose principles of mass customization to be of high relevance for companies that

need to master the challenge of bridging efficiency and flexibility. Mass customiza-

tion has become a widely accepted strategic option for companies to generate economic

benefits in dynamic markets in which, for example, customer needs become increasingly

heterogeneous and product life-cycles are getting shorter (ElMaraghy et al., 2013). Mass

Customization corresponds to ”the technologies and systems to deliver goods and ser-

vices that meet individual customers needs with near mass production efficiency” (Tseng

& Jiao, 2001, p. 685).

However, implementing mass customization is often associated with the risk of rais-

ing manufacturing costs and increased production cycle-times (Salvador et al., 2004;

Squire et al., 2006; Salvador et al., 2009). Companies need to develop and implement

a so called mass customization capability in order to prevent these risks and, in turn

to realize the potential of a mass customization strategy (Tu et al., 2001, 2004a). The

mass customization capability constitutes a company’s ability to reorganize and recon-

figure its manufacturing resources quickly to be able to efficiently produce and deliver

a wide range of different product variants on large scale (Tu et al., 2001; Squire et al.,

2006). Thus, it reflects a company’s ability to orchestrate the flexible exploitation of

its manufacturing resources in order to provide high variety without losing operational

excellence (Ahlstrom & Westbrook, 1999; Berry & Cooper, 1999; Tu et al., 2001; Squire

et al., 2006; Huang et al., 2008; ElMaraghy et al., 2013). Studies suggest that a higher

level of the mass customization capability is associated with reduced manufacturing costs

and decreased manufacturing cycle times, thus, with a higher operational efficiency (Tu

et al., 2001). Moreover, the mass customization capability is also associated with short

response times and low inventory costs (Feitzinger & Lee, 1997).

An antecedent for achieving the necessary flexibility for the efficient production of

a wide variety of products, and thus for the mass customization capability, is flexibility

on strategic level (Kotha, 1995; Squire et al., 2006; Salvador et al., 2009). Strategic

flexibility is associated with the flexible allocation and reallocation of resources, thereby

facilitating modular manufacturing processes and architectures, which are a crucial part

of mass customization manufacturing systems and the mass customization capability

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(Zhou & Wu, 2010; ElMaraghy et al., 2013). Moreover, strategic flexibility is related to

the flexibility of modular product designs (Zhou & Wu, 2010), thereby fostering efficient

large-scale high-variety production and the mass customization capability (Huang et al.,

2008). Finally, the reallocation of organizational resources, which is associated with

strategic flexibility, helps to increase flexibility in manufacturing and thus, supports

flexible manufacturing strategies such as mass customization

Relying on this discussion, we suggest that mass customization capability is pos-

itively associated with strategic flexibility and operational efficiency, and furthermore,

mediates the relationship between these two constructs.

Hypothesis 1: Strategic flexibility is positively associated with mass customization

capability (H1a), which is, in turn, positively related to operational excellence (H1b).

Thus, mass customization capability mediates the relationship between strategic flexi-

bility and operational excellence (H1c).

Strategic flexibility, technological capability, and operational efficiency

Technological capability describes the capacity of a company to make use of its own

technological resources by ”[...] combining/ recombining components, linkages between

the components, methods, processes and techniques, and underpinning core concepts to

offer products” (Afuah, 2002, p. 172). Moreover, technological capability is referred

to the identification of new technological trends, experimentation with new designs,

and innovation (Rosenkopf & Nerkar, 2001). In this line, it is argued that technology-

related capabilities are necessary in order to be able to acquire, develop, and produce

technological resources (ranging from patents, knowledge stock, and product designs to

skilled personal), which in turn improve company’s responsiveness to a fast changing

technological landscape (Song et al., 2005). Technological-related capabilities have been

shown to enable companies to achieve high levels of performance (Clark & Fujimoto,

1991; Pisano, 1994; Song et al., 2005). As technological capability emphasizes the flexible

usage of technologies for a faster creation and improvement of products and processes,

it boosts improvements and refinements of existing process and product technologies

(Zhou & Wu, 2010). Furthermore, technological capability fosters exploitation at an

accelerating rate (Zhou & Wu, 2010), which in turn is positively related to company

performance through process innovation intensity (He & Wong, 2004).

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However, technological capability by its own is not sufficient to deliver customer

value and drive innovation (Teece, 1986). Rather, companies need to possess a comple-

menting dynamic capability that allows for flexible reorganization of resources to meet

changes in the environment (Teece et al., 1997; Eisenhardt & Martin, 2000; Ozcelik

& Taymaz, 2004). Focusing on the flexible use and allocation of company resources,

strategic flexibility fosters the creation of surroundings that allow for better acquisition

and usage of information (Matthyssens et al., 2005) and enables companies to use new

technologies (Worren et al., 2002), thereby broadening the value of a company’s techno-

logical capability (Zhou & Wu, 2010). Flexibility in coordination among business units

also helps a company to break down its institutionalized technological processes and

explore new alternatives (Gilbert, 2005). Strategic flexibility creates an environment in

which the company can better assimilate and use new information, for example about

new technologies (Matthyssens et al., 2005), thereby strengthening the company’s tech-

nological capability. Following this argumentation, we assume that strategic flexibility

can help to enlarge the existing technological capability of a company and makes it more

efficient regarding technical information procurement and use.

Hypothesis 2: Strategic flexibility is positively associated with technological capa-

bility (H2a), which is, in turn, positively related to operational excellence (H2b). Thus,

technological capability mediates the relationship between strategic flexibility and oper-

ational excellence (H2c).

Research method

Data collection and sample

For the analysis of the proposed model, we used data from a survey conducted by a

team of researchers from the University of San Diego, United States of America. For

this survey, a structured web-based questionnaire with closed questions was utilized.

A commercial database was used to select companies for the survey. To increase the

generalizability of the results, manufacturing companies from all branches of industry

were admitted. The questionnaires were send to top-level managers of the identified

manufacturing companies, whereby every company was represented by one participant

only. Top-level executives were chosen as key respondents as they possess the required

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knowledge about the company and its performance (Goodale et al., 2011). The managers

were approached with an e-mail invitation to participate in the survey. Overall, 181

managers participated, while 95 responses were excluded from further steps by virtue of

missing data, unengaged answers or key respondent criteria mismatches. This results in

a response rate of 47.5% (n = 86). Descriptive statistics are reported in Table 1.1.

Table 1.1: Descriptive statistics

Job Title Percentage(n = 86)

Chief executive officer (CEO) 20.9%Chief operating officer 1.2%Chief financial officer 3.5%Executive Director 1.2%President 24.4%Chairman 4.7%Director 5.8%Senior vice president 1.2%Vice president 16.3%Managing Director 1.2%Senior Manager 8.1%Chairman & CEO 2.3%President & CEO 5.8%Other positions 3.5%

GICS division (Code) Percentage

Energy (10) 4.7%Materials (15) 19.8%Industrials (20) 19.8%Consumer discretionary (25) 20.9%Consumer staples (30) 7.0%Health care (35) 8.1%Financials (40) 4.7%Information technologies (45) 15.1%Telecommunication services (50) -Utilities (55) -

Firm size (number of full-time employees)

1 - 10 19.8%11 - 50 17.4%51 - 250 32.6%251 - 1.000 12.8%1.001 - 50.000 14.0%> 50.000 3.5%

Notes. GICS = Global Industry ClassificationStandard.

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A comparison of early and late respondents did not indicate any statistically signifi-

cant differences in the mean values. Thus, non-response bias is not an issue in this study

(Armstrong & Overton, 1977). We performed a single factor test suggested by Harman

(1967) in order to test for common method bias. Results of a explorative factor analysis

do not provide a single factor that accounts for most of the covariance among the items

indicating that common method variance is not an issue in this study.

Measures

Established scales from literature were adopted, using seven-point scales for the mea-

surement (unless stated otherwise), anchored by 1 = strongly agree and 7 = strongly

disagree. The dependent variable operational efficiency (OE) was measured using a

second-order construct, which consists of two constructs for time-based performance

(TBP; five-item scale) and cost-based performance (CBP; three-item scale) (Yeung,

2008). The independent variable strategic flexibility (SF) was operationalized using a

adapted six-item scale from Zhou and Wu (2010). This scale measures the strategic

emphasizes of companies in response to changing environments. A seven-item scale was

adopted to measure the mass customization capability (MCC) of a company (Huang

et al., 2010), which captures the ability of a company to efficiently produce customized

products on a large scale. Finally, the used technology capability (TC) scale comprising

five items was adopted from Zhou and Wu (2010), measuring a company’s capability

to deal with new technologies relatively to major competitors.

Additionally, two control variables were included in order to improve the explana-

tory power of the study. We controlled for company size using the number of full-time

employees, as larger companies have a tendency to higher levels of standardization, and

thus to lower level of flexibility. Furthermore, we controlled for company age, because

older companies tend to be focused on increasing operational efficiency, rather than flex-

ibility (Chen & Hambrick, 1995). All scale items for the construct measures are reported

in the Appendix 1.

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Analyses

The analyses were conducted using the partial least squares approach for structural equa-

tion modeling (PLS-SEM). Therefore, we employed SmartPLS version 2.0 M3 (Ringle

et al., 2005), as this software is able to deal with small sample sizes (Peng & Lai, 2012).

Furthermore, structural equation modeling using PLS is particularly suitable for ana-

lyzing models containing hierarchical latent variables (Wetzels et al., 2009), as it is the

case in this study. For the analysis of the hypothesized mediating roles of the mass

customization capability and the technological capability, we applied mediation tests

suggested by Arioan, Goodman, and Sobel (Aroian, 1947; Goodman, 1960; Sobel,

1982).

Validity and reliability of the scales

Following Hair et al. (2012), the outer model was evaluated in terms of (1) item

reliability, (2) internal consistency reliability, (3) convergent validity, (4) discriminant

validity, and (5) cross loadings. After the exclusion of three items, the analysis showed

that item reliability was ensured as all remaining items exceed the threshold value of .7

(Hulland, 1999). The internal consistency reliability of the constructs was acceptable as

the composite reliability values for the constructs are above .7 (Bagozzi & Yi, 1988).

Finally, on construct level the convergent validity was evaluated by examining the

values of the average variance extracted (AVE). Results provided proof as all constructs

exceeded the threshold of .5 (Bagozzi & Yi, 1988), with one exception for the operational

efficiency construct. However, with a AVE value of .49 is very close to the threshold, we

used the construct unchanged for the following analyses. Discriminant validity between

all constructs was indicated by fulfilling the Fornell-Larcker-Criterion (Fornell

& Larcker, 1981).1 We tested for cross loadings and found that each indicator loaded

highest on the construct it is intended to measure. Results of the outer model evaluation

are reported in Table 1.2 and Table 1.3.

1The correlation between the higher-order construct for TBP and the lower-order construct for com-pany performance did not meet the criterion, which is, however, not critical for hierarchical latentvariables (Hair et al., 2013).

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Table 1.2: Results of the measurement validation

Min Max Mean SD (1) (2) (3) (4) (5) (6) (7)

CBP1 1 7 4.62 1.50 .77 .22 .12 .12 .10 -.04 -.20CBP2 1 7 4.45 1.48 .89 .20 .08 .26 .13 0 -.25CBP3 1 7 4.20 1.42 .83 .13 .07 .43 .09 .07 -.04MCC1 1 7 4.63 2.04 .14 .84 .49 .04 .22 .15 .10MCC2 1 7 4.49 1.67 .18 .86 .39 .15 .14 -.04 -.10MCC3 1 7 4.45 1.75 .35 .73 .32 .40 .15 .10 -.04MCC4 1 7 4.59 1.87 .17 .90 .54 .14 .27 .06 .12MCC5 1 7 5.48 1.42 .06 .82 .36 .26 .08 .12 -.15MCC6∗ - - - - - - - - - -MCC7 1 7 5.20 1.66 .14 .85 .30 .19 .12 -.01 -.10SF1∗ - - - - - - - - - - -SF2 1 7 4.86 1.35 -.01 .57 .81 .01 .26 .14 .16SF3 1 7 4.97 1.47 .09 .52 .87 .09 .35 -.04 .06SF4 1 7 4.91 1.14 .10 .18 .72 .09 .44 -.14 .14SF5 1 7 5.06 1.28 .17 .18 .77 .16 .39 .06 .18SF6∗ - - - - - - - - - - -TBP1 1 7 5.38 1.47 .37 .17 .18 .85 .13 .14 -.19TBP2 1 7 5.28 1.55 .26 .11 .09 .87 .12 .10 -.19TBP3 1 7 4.77 1.64 .32 .35 .05 .79 .09 .15 -.15TBP4 1 7 4.58 1.68 .22 .14 .01 .90 .26 .11 -.18TBP5 1 7 5.12 1.40 .31 .23 .09 .91 .25 .15 -.01TC1 2 7 5.17 1.37 .10 .25 .41 .26 .93 -.04 .04TC2 2 7 5.28 1.32 .17 .23 .34 .27 .92 -.08 -.03TC3 2 7 5.20 1.40 .10 .19 .41 .19 .95 -.06 -.01TC4 1 7 4.90 1.46 .12 .15 .43 .16 .91 -.07 .08TC5 1 7 4.88 1.51 .09 .11 .43 .05 .90 -.09 .05Age 1 200 58.27 43.33 .02 .08 0.01 .16 -.07 1.00 .30Size - - 2.94 1.41 -.18 -.03 .16 -.17 .03 .30 1.00

Notes. n = 86. All loadings below .4 are not displayed.(1) = cost-based performance. (2) = mass customization capability.(3) = strategic flexibility. (4) = time-based performance.(5) = technological capability. (6) = company age. (7) = company size.∗Dropped due to measurement issues. SD = standard deviation.

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Table 1.3: Evaluation of the measurement constructs

CR AVE (1) (2) (3) (4) (5) (6) (7) (8)

(1) Cost-based performance∗ .87 .69 .83(2) Operational efficiency .88 .49 .67 .70(3) Mass customization capability .93 .70 .21 .28 .85(4) Strategic flexibility .87 .63 .10 .12 .49 .80(5) Time-based performance∗ .92 .70 .35 .93 .24 .10 .84(6) Technological capability .97 .85 .13 .21 .20 .44 .20 .92(7) Company size - - .02 .13 .08 0.01 .16 -.07 n.a.(8) Company age - - -.18 -.21 -.03 .16 -.16 .03 .30 n.a.

Notes. n = 86. CR = composite reliability. AVE = average variance extracted. Square root ofAVE on the diagonal. Off-diagonal values are correlations among constructs.∗Lower-order component of the higher-order company performance construct.

Common method bias

Common method variance can bias the results when both independent and dependent

measures are obtained from the same source, as it is the case in this study. To address

the issue of common method variance, we used Harman’s one-factor test (in accordance

with Podsakoff & Organ, 1986). A exploratory factor analysis (EFA) suggest five factors

(one each for CBP, TBP, MCC, SF, and TC) with eigenvalues greater than one, which

together accounted for 75.8% of the total variance. The first factor accounted for 30.16%

of the total variance. As we obtained a multiple factor structure and, furthermore, none

of the factors accounts the majority of the covariance among the measures, we could

conclude that common method bias was not an issue in this study.

Results

In terms of the evaluation of inner models of PLS-SEM, only non-parametric evaluation

criteria can be applied (Wetzels et al., 2009). Following Hair et al. (2012), (1) the

explained variance of the endogenous construct, (2) the predictive relevance, (3) the

effect size, and (4) the path coefficient estimates are consulted to evaluate the inner

model. The estimation results for the path coefficients and explained variances of the

structural equation model are illustrated in Figure 1.2. Moreover, coefficients for all

paths, including the covariates, are reported in Table 1.4. We obtained sufficient values

for the R2 and Q2 (.09) of operational efficiency. Furthermore, findings reveal positive

associations between strategic flexibility and mass customization capability as well as

80

technological capability, thereby support for H1a and H2a. Furthermore, as hypothe-

sized (H1b and H1c), mass customization capability and technological capability each is

positively associated with operational efficiency.

Figure 1.2: Results of the structural equation model estimation

Table 1.4: Results of the PLS-SEM estimation

Path Coefficient (SE) T-value

OE ⇒ CBP .67 (.08) 8.69 (∗∗∗)OE ⇒ TBP .93 (.02) 49.02 (∗∗∗)SF ⇒ MCC .51 (.09) 5.50 (∗∗∗)SF ⇒ TC .44 (.11) 4.12 (∗∗∗)SF ⇒ OE -.04 (.13) 0.32 (n.s.)MCC ⇒ OE .23 (.13) 1.77 (∗)TC ⇒ OE .20 (.12) 1.75 (∗)Age ⇒ OE .21 (.10) 2.14 (∗)Age ⇒ MCC .12 (.11) 1.03 (n.s.)Age ⇒ TC -.07 (.09) .72 (n.s.)Size ⇒ OE -.26 (.11) 2.43 (∗)Size ⇒ MCC -.14 (.10) 1.40 (n.s.)Size ⇒ TC -.02 (.11) .22 (n.s.)

Notes. n = 86. ∗p < .05, ∗∗p < .01,∗∗∗p < .001, n.s. = not significant.Critical t-value: 1.66 → p = 0.1,1.98 → p = 0.05, 2.63 → p = 0.01,3.40 → p = 0.001. Age = company age.Size = company size. SE = standard error.

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For the analysis of the mediation effects, we used a Aroian test, a Goodman

test, and a Sobel test (Aroian, 1947; Goodman, 1960; Sobel, 1982; Baron & Kenny,

1986; MacKinnon et al., 1995). For MCC, the three test statistics are significant (p

< .1), thus providing support for H1c. For TC, all three mediation test statistics are

non-significant, indicating that TC does not meditate the strategic flexibility-operations

efficiency relationship. Therefore, Hypothesis 2c is not supported by our results. All

results of the mediation tests are reported in Table 1.5.

Table 1.5: Results of the mediation analyses

MCC TC

Test statistic SE P-value Test statistic SE P-value

Sobel Test 1.68 .07 .092 1.61 .06 .108Aroian Test 1.66 .07 .097 1.57 .06 .116Goodman Test 1.71 .07 .087 1.65 .05 .100

Notes. P-values are two-tailed probabilities. SE = Standard error.

Discussion and implications

The purpose of this study is to broadening our understanding about how to mitigate the

tradeoff between strategic flexibility and operational efficiency, which both are important

to remain competitive in dynamic environments (Eisenhardt et al., 2010). Based on the

literature, we emphasize the role of operational capabilities which might help to reduce

this tradeoff.

Our model suggests that mass customization capability and technological capability

represent two capabilities on operational level that are able to mitigate the above men-

tioned tradeoff. However, the results of the analysis of our data, which were obtained

from a survey among top-level managers of manufacturing companies in the US, does

not provide evidence for all of our hypotheses. The analysis confirms all parts of our first

hypothesis: as suggested, strategic flexibility is positively related to the mass customiza-

tion capability which in turn is positively related to operational efficiency. Furthermore,

results provide evidence for the mediating role of mass customization capability with

regard to the direct relationship between the flexibility and efficiency constructs. The

second hypothesis is only partial consistent with the results of the analysis. Findings

support the hypothesized positive relation between strategic flexibility and technological

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capability as well as between technological capability and operational efficiency. How-

ever, the mediation test statistics are not significant and do not provide support for

this part of our second hypothesis. It should be noticed that all three mediation tests

are very close to the upper significance threshold of p < .1. A possible explanation for

this result might be a uncovered relationship between technological capability and mass

customization capability. A first indication that such a direct relationship between these

constructs may exist provides the following consideration. There is empirical evidence

that mass customization capability benefits from effective process technology implemen-

tation (Huang et al., 2010) and technological capability in turn is related to deploy,

utilize, and master various technological resources (Zhou & Wu, 2010), such that the

constructs might directly be related to each other. However, this requires an empirical

investigation in a more detailed way, which seems to be a desirable objective for future

research.

This research provides valuable insights to both academics and business practition-

ers by improving our understanding how the tradeoff between strategic flexibility and

operational efficiency can be mitigated. Theoretically, our study contributes to the RBV

and its dynamic extension (Teece et al., 1997; Winter, 2003; Helfat et al., 2009). By

addressing the gap how to balance flexibility and efficiency (Eisenhardt et al., 2010), we

empirically verify that a capability on the operational level – the mass customization

capability – helps to mitigate the tradeoff between the required dynamic capabilities on

the strategic level in fast-changing business environments, i.e. strategic flexibility, and

operational efficiency. This provides confirmation for the repeatedly stated view that

dynamic capabilities on strategic level needs to be complemented by suiting capabilities

on the operational level which effectively and efficiently perform their functions (Eisen-

hardt & Martin, 2000; Teece, 2007; Helfat et al., 2009; Helfat & Winter, 2011), thereby

extending the literature on dynamic capabilities.

The findings of this study are also relevant for the literature on mass customization

and high-variety product strategies as well as for companies implementing a mass cus-

tomization strategy and for companies that are challenged to continuously adapt their

product offerings to keep up with ever changing business environments. First, our results

confirm previous research which state that mass customization capability or related ca-

pabilities dedicated to the implementation of flexible but efficient manufacturing systems

for high-variety product offerings help to increase operational efficiency (Boyer, 1999;

83

Tu et al., 2001, 2004a; Huang et al., 2008). Second, findings reveal that developing and

implementing flexibility on the strategic level enables companies to flexibly and quickly

(re-) allocate organizational resources, thereby fostering their mass customization ca-

pability. Thus, strategic flexibility indirectly helps to increase operational efficiency,

i.e. time-based and cost-based performance, through the mass customization capabil-

ity. This should be noticed by managers, that it seems beneficial to complement this

mass customization capability by specific organizational capabilities on strategic level,

i.e. strategic flexibility.

Managers should also carefully design the decision making processes as well as the

coordination of the development and implementation of these capabilities. Since they

are located on different organizational levels, managers should take a holistic view and

integrate perspectives from all these different levels in order to achieve a good coordi-

nation of these capabilities. The necessary collaboration of managers from operations

and strategy during these processes might require specific work routines and processes

in order to ensure that the targeted benefits of coordinating the capabilities can be

realized. These questions relating to the successful development, implementation and

coordination of these capabilities provides interesting directions for future research.

Limitations and future research

Despite insights grained through our findings, there are some limitations inherent in this

study, which limit its interpretability and generalizability, and which provide starting

points for future research.

Building on the theory, we hypothesize that technological capability meditates the

relation between strategic flexibility and operational efficiency. However, data does

not provide support for this hypothesis, and thus, it was rejected. We suggest that

future research should investigate the relations between the constructs in the model in a

more detailed way. Thereby, the influence of potential moderators, or further mediating

variables should be taken into account, which might suppress the mediating effect of

technological capability.

Although cross-sectional studies are well accepted in the literature (e.g. Huang et al.,

2010; Lisboa et al., 2011), it is only a snapshot of a company’s status quo which limits

84

our ability to fully explore and understand causality between the factors. Therefore,

findings need to be replicated elsewhere, preferably by using longitudinal panel data.

Such data allows researcher to detect and control for time lags, and thus, for the changes

in the value of the dependent variable (Hsiao, 2003). Moreover, the associated larger

sample helps to reduce the threat of collinearity among the independent variables (Hsiao,

2003). Therefore, tracking variables longitudinally helps researcher to get a richer and

more valid understanding of the relationships between the variables of interest.

In addition, all data obtained are based on the perception of the single respondents,

which is well established in the literature due to the difficulties to acquire objective

data. However, self-reported data could be constraint by common method bias, there-

fore Harman’s one-factor test was performed which reveals that the effect of common

method variance is likely to be negligible. It would be beneficial if future research ob-

taining objective data to further reduce the probability of this potential problem. Using

longitudinal panel data, which based on answers by different respondents or the same

participants with time lags between the surveys, would also help to reduce the thread

of common method bias (Doty & Glick, 1998).

In this study, we could only assume that the chosen industry sectors for the sur-

vey are characterized by high levels of dynamic; however, there is no indication for it.

Although the obtained the industry affiliation of the companies in our sample would

allow to control for the industry dynamic, at least approximately, we are not able to

integrate these control variables into our model since the sample size is to small. A

well-established rule of thumb for robust PLS-SEM estimations claims that the number

of path relations directed at a construct needs to be smaller or equal to one-tenth of

the sample size (Barclay et al., 1995; Hair et al., 2012). This criterion cannot be met

when integrating the industry controls into our model (industry controls would account

for all of the 8 paths available). Therefore, the results and indications of this study

are limited and need to be interpreted with caution. We suggest that future research

needs to replicate our results on a sample of companies which are verifiable acting in

dynamic business environments. Preferably, such replication studies control for different

dimensions of the dynamic of environmental change, such as pace, predictability, com-

plexity, and ambiguity (Davis et al., 2009), which have been shown to be relevant in

related contexts (e.g. Gatignon, 1997; Davis et al., 2009; Dayan & Di Benedetto, 2011;

85

Volberda et al., 2012). Beside this, there might be further control variables that are

relevant in this context.

Finally, based on theory, we take only a very limited number of factors into account,

thus, our model is by no means complete. There might be other capabilities on other

levels of a company that also influence the tradeoff between flexibility and efficiency or, as

argued above, there might be uncovered relationships between the variables in our model.

Thus, follow-up research should include additional factors from other levels as well as

further controls in the analysis, for example measures for capabilities related to supply

chain flexibility as these are positively associated with manufacturing flexibility and

company performance (Calantone et al., 1999) or the above mentioned control measures

for environmental dynamics.

86

Appendix

Appendix 1: Scale items for construct measures

Strategic flexibility (Zhou & Wu, 2010)

SF1 The flexible allocation of marketing resources (including advertising, pro-motion and distribution resources) to market a diverse line of products.

SF2 The flexible allocation of production resources to manufacture a broadrange of product variations.

SF3 The flexibility of product design (such as modular product design) to sup-port a broad range of potential product applications.

SF4 The redefinition of product strategies in terms of target market segments.

SF5 The reallocation of resources for developing, manufacturing,and deliveringproducts to targeted markets.

SF6 The reallocation of organizational resources to support the firm’s intendedproduct strategies.

Mass customization capability (Huang et al., 2008)

MCC1 We are highly capable of large-scale product customization.

MCC2 We can easily add significant product variety without increasing costs.

MCC3 Our setup costs when changing from one product to another are very low.

MCC4 We can customize products while maintaining high volume.

MCC5 We can add product variety without sacrificing quality.

MCC6 We tend to produce standardized products when ever possible. (reversecoded)

MCC7 Our capability for responding quickly to customization requirements is veryhigh.

Technological capability (Zhou & Wu, 2010)

Compared to your major competitors, how would you evaluate your firm’scapabilities in the following areas:

TC1 Acquiring important information about new technologies.

TC2 Identifying opportunities related to new technologies.

TC3 Responding to technology changes.

TC4 Mastering state-of-art technologies.

TC5 Developing a series of innovations constantly.


87


Cost-based efficiency (Yeung, 2008)

Our firm (...)

CBP1 ...has very low total quality costs relative to the total output.

CBP2 ...reveals low engineering change rates in the production stage.

CBP3 ...has very low unit costs of manufacturing.

Time-based efficiency (Yeung, 2008)

Our firm (...)

TBP1 ...reveals outstanding delivery speed and reliability.

TBP2 ...is famous for the timeliness of delivery.

TBP3 ...has a very short manufacturing lead time.

TBP4 ...reveals a high inventory turnover rate.

TBP5 ...has an excellent production cycle time.

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Paper 2

Measuring firms’ capabilities for

mass customization: Construction

of a formative measurement index

Abstract: Today, many industries, especially in a business-to-business context, witness

a shift in customer demand structures towards increasing customer need heterogeneity.

In consequence, more and more firms are forced to develop high-variety product offer-

ings. Being confronted with these changing business environments, manufacturers need

to establish new suitable business models, such as mass customization. However, adapt-

ing a respective mass customization business model for high-variety production demands

profound organizational change and many firms fail in transforming their businesses ac-

cordingly. For this reason, this paper identifies a gap in existing management research

for mass customization with regard to the description of the necessary transformation

process, during which the application of certain managerial activities or organizational

routines help practitioners step by step to become better mass customizers. In order to

address this research gap, this paper develops a formative measurement index for mass

customization. As a result of this development process the paper extends and redefines

existing research on mass customization capabilities, derives a repository of manage-

rial activities and organizational routines for implementing mass customization business

models and, lastly, develops a formative measurement index for mass customization for

future research in the field.

97

98

Keywords: Mass customization, formative index development, causal indicators,

measurement.

Status:

• Steiner, F. and M. Wellige (2014). Strategic Capabilities to Manage High-Variety

Production Environments: The Role of Underlying Activities and Organizational

Resources. Proceedings of the 7th World Conference on Mass Customization,

Personalization, and Co-Creation (MCPC 2014), Aalborg, Denmark, February

4th - 7th, 2014. T. D. Brunoe, K. Nielsen, K. A. Joergensen and S. B. Taps,

Springer International Publishing: 487-504. (Early version)

• Nominated for the Best Student Paper Award 2014 of the Operations

Management Division of the Academy of Management.

• Accepted for the publication in the Best Paper Proceedings of the Academy of

Management Meeting 2014.

• Later version of this paper is already in the publishing process.

Presented at:

• Mass Customization Workshop 2013, Politecnico di Milano, February 2013,

Milano, Italy.

• 3rd IMR PhD day, Radboud University Nijmegen, May 2013, Nijmegen, the

Netherlands.

• 7th World Conference on Mass Customization, Personalization, and Co-Creation

2014, February 2014, Aalborg, Denmark.

• Academy of Management Meeting 2014, August 2014, Philadelphia, United

States of America.

99

Introduction

In a many industrial markets, firms recently have been confronted with changing market

conditions. One major development that can be observed in this context is a general

shift in customer demand structures: customer needs are becoming increasingly hetero-

geneous. In consequence, the demand for customized goods and services that address

individual customer needs is strongly increasing (Franke et al., 2009). This trend seems

to be of particular importance in the domain of industrial goods (Ellis, 2010, p. 17).

Conceivably, the differentiation of customer needs impacts the business environment of

manufacturers. Thus, firms are facing new market requirements. This manifests itself,

for example, in an increasing number of product variants in the product offerings of

many providers of industrial good (ElMaraghy et al., 2013).

Being confronted with these changing business environments, manufacturers need

to establish new business models that are capable of dealing with high levels of hetero-

geneity and that enable firms to provide high-variety product offerings efficiently. In

this context, the concept of mass customization (MC) offers a strategic approach that

aims at efficiently offering many variants of a product and thereby tries to meet the

heterogeneous needs of individual customers (Pine, 1993, p. 44). However, adapting a

company to the requirements of MC demands profound organizational change (Duray,

2002; Piller, 2004; Rungtusanatham & Salvador, 2008). Existing research suggests that

companies have to develop certain strategic capabilities in order to be able to successfully

implement such a business strategy. In order to provide guidelines for the implementa-

tion of MC, research discusses potential success factors or capabilities that are intended

to facilitate the transformation process (cf. Tu et al., 2004b; Salvador et al., 2009; Fogli-

atto et al., 2012; Harzer, 2013). For example, a broad body of literature highlights the

need for flexible but also efficient manufacturing processes for the realization of large

numbers of product variants (Ahlstrom & Westbrook, 1999; Swaminathan, 2001; Tu

et al., 2001, 2004a). Beyond the discussion of such success factors, Salvador et al.

(2009) suggest a rather sophisticated and widely adopted framework of three strategic

capabilities: solution space development (SSD), robust process design (RPD) and choice

navigation (CN).

Even though there is a broad body of theoretical groundwork concerning the im-

plementation and application of MC (Fogliatto et al., 2012), we believe that there are

100

certain shortcomings of the MC research that might hinder companies in transferring

the existing theoretical findings into business practice. In the course of this paper we

identify three such shortcomings: first, the general understanding of mass customiza-

tion as a mere performance ideal rather than a process of developing certain strategic

capabilities in terms of the resource-based view (RBV) of the firm (cf. Penrose, 1959;

Wernerfelt, 1984; Teece et al., 1997; Eisenhardt & Martin, 2000) might hinder the suc-

cessful realization of the concept. Second, the abstraction level of existing research seems

to be too detached from the application of the business model, so that there are only

few guidelines that support practitioners by suggesting actual management activities

that could be initiated to strengthen the mass customization capabilities of a firm. In

our opinion this manifests itself in the fact that existing research puts a lot of effort

in defining strategic capabilities for MC, but mostly neglects a sufficient differentiation

between capabilities and their constituent, underlying resources as described in RBV

theory (Amit & Schoemaker, 1993, p. 35). Subsequently, we see a gap in existing

management research for mass customization with regard to the application of certain

managerial activities or organizational routines help practitioners step by step to be-

come better mass customizers. Third, most empirical studies that address the issue of

developing strategic capabilities for MC are concentrated on a single capability, which

is then investigated in an isolated manner. In this context, we see a need for research

that takes a holistic perspective across all required strategic capabilities.

In order to address the research gap described above, this paper develops a forma-

tive measurement index for mass customization according to the guidelines suggested

by Diamantopoulos and Winklhofer (2001). A formative index consists of a set

of activities that are independent of each other and that cause or form a certain la-

tent variable in an additive manner (Diamantopoulos, 1999). Thus, it allows enriching

the existing research on mass customization capabilities with detailed insights on the

level of managerial activities and organizational routines. In consequence, we believe

that this approach is appropriate for addressing the above-mentioned shortcomings in

MC research, as it supports mass customization practitioners on an operational level.

Thereby, we make several important contributions to research on MC or high-variety

production environments in general: 1) This research project extends and refines the

definitions of strategic capabilities for mass customization (Salvador et al., 2009) by

101

introducing two sub-categories for each of the three capabilities. 2) Based on litera-

ture reviews and expert workshops the paper provides an extensive list of managerial

activities. This repository of approximately 100 activities and routines can serve as

potential building blocks for strengthening a company’s ability to provide a mass cus-

tomization offering. 3) Finally, the paper develops the formative measurement index for

mass customization itself. Thereby, the index is based on the framework of strategic

capabilities for mass customization mentioned above (Salvador et al., 2009). The index

could emerge as a valuable contribution to research on mass customization, as it does

not only allow to measure the performance level of the mass customization capabilities

of a business, but it also provides immediate management support, as it identifies those

management activities that practitioners could apply in order to improve their existing

mass customization offering.

Following this introduction, our paper is structured into three subsequent sections:

Section 2 discusses the theoretical background of mass customization research, identifies

the research gap that we address with this paper, and describes how the development

of a formative index could mitigate the effects of the above-mentioned shortcomings.

Afterwards, Section 3 describes the applied methodology and presents the results of the

index development. In conclusion, Section 4 discusses the results, provides managerial

implications and discusses limitations of the study.

Mass customization - a strategy for high-variety markets

The market structure in many product domains has changed dramatically in the last

decades: on the demand side, markets have become increasingly globalized (Friedman,

2007) and manufacturers today are facing varying product requirements in different

market regions (ElMaraghy et al., 2013). Additionally, customer needs in general seem

to become more and more heterogeneous and customized products are strongly gaining

importance (Franke et al., 2009). This trend has particular relevance in industrial goods

markets, where customers usually have clearly defined specifications (Ellis, 2010, p.

17) and typically show low levels of flexibility concerning their product requirements

(Lancaster, 1990; Zhang & Tseng, 2009). On the supply side of the markets, significant

changes can be observed as well: the emergence of new materials and technologies enable

the manufacturing of new and different product features (ElMaraghy et al., 2013) and

102

allow an economically efficient production of very small lot sizes (Blecker et al., 2006b,

p. 37f.). In consequence, a dramatic increase in the number of product variants can be

observed recently in many industries (ElMaraghy et al., 2013).

In this context, the strategy of MC is an approach that addresses exactly the above-

mentioned conditions of heterogeneous customer needs and high-variety product offer-

ings. The term ”mass customization” was introduced by Pine, who defines it as ”devel-

oping, producing, marketing and delivering affordable goods and services with enough

variety and customization that nearly everyone finds exactly what they want” (Pine,

1993, p. 44). This definition clearly highlights the idea of customization: firms should

try to offer a broad variety of products, so that individual customers have a better

chance of finding products and services that correspond to their needs. This procedure

builds on the well-established expectancy disconfirmation model, which claims that ev-

ery customer envisions an ideal product, which will be used as a benchmark for all

products that are available on the market and that customers are more satisfied with

a product, the closer it is to their ideal product (Oliver, 1980; Bearden & Teel, 1983;

LaBarbera & Mazursky, 1983; Oliver & DeSarbo, 1988; Yi, 1990). Thereby, the approach

follows Chamberlin’s (1962) theory of monopolistic competition, according to which

customers can gain the increment of utility of a customized good that better fits their

needs than any available standard product (Piller, 2004). Subsequently, customers per-

ceive higher value in products that closely meet their individual preferences and existing

research has shown that the customers also honor this additional value with an increase

in their willingness-to-pay (Franke & Piller, 2004). Therefore, customizing products to

the needs of individual customers, as suggested in the mass customization definition

by Pine (1993, p. 44), might lead to increased revenues. Thus, diverse customer re-

quirements must not necessarily be regarded as a threat, but should be recognized as a

potential business opportunity, as heterogeneity in customer needs can be turned into

additional revenues (Piller & Steiner, 2013).

However, companies can only benefit from this increase in revenues, if the cost of

providing the customized goods does not increase even more than the revenues. This

notion is captured in the definition by Tseng et al., who define mass customization as

a business strategy that ”[...] aims at best satisfying customers’ individual needs with

near mass production efficiency” (Tseng et al., 1996, p. 153). This aspect of efficiency in

the production and distribution of customized goods and services is crucial for the idea of

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mass customization. The definition clarifies that offering limitless choice is economically

unfeasible (Piller, 2004, p. 316). Therefore, variety should be offered only for those

product attributes, along which customer needs diverge and that can be aligned with

the existing manufacturing capabilities of the firm (Zhang & Tseng, 2007; Salvador et al.,

2009). Subsequently, companies, which are targeting the exploitation of heterogeneous

customer needs, have to develop a product offering that bridges the diverse customer

needs on the one hand and the manufacturing capabilities of the company on the other

hand (Pil & Holweg, 2004, p. 394).

Embedding mass customization in the resource-based view

In recent years, a growing number of companies has established business models and

strategies, which are geared towards product customization and high-variety production

(Broekhuizen & Alsem, 2002; ElMaraghy et al., 2013). Numerous examples of successful

applications, ranging from the automobile industry (Salvador et al., 2009) and engi-

neered products (Lu et al., 2009) to electronics (Comstock et al., 2004; Partanen &

Haapasalo, 2004), demonstrate the high relevance as well as the practicability of such

strategies. However, the examples also reveal that some companies are more successful

than others in the realization of a MC strategy. The resource-based view of the firm

regards the existence and composition of certain resources as the source of firm success

and explains above-mentioned differences in firm performance by an imperfect distribu-

tion of such resources across companies (Penrose, 1959; Wernerfelt, 1984; Teece et al.,

1997; Eisenhardt & Martin, 2000). Thus, each company represents a unique bundle of re-

sources that determines its competitive position (Amit & Schoemaker, 1993; Day, 1994).

Thereby, literature provides various definitions of resources: Wernerfelt (1984), for

example, defines resources rather broadly as everything that could serve as a strength or

weakness for a given firm. In order to provide more concrete examples the author lists

”brand names, in-house knowledge of technology, employment of skilled personnel, trade

contacts, machinery, efficient procedures [and] capital” as potential resources (Werner-

felt, 1984, p. 172). Barney (1991) extends this definition by including capabilities and

organizational processes. Amit and Schoemaker (Amit & Schoemaker, 1993, p.33)

also see ”resources and capabilities to be crucial in explaining a firm’s performance”, but

they differentiate between the two terms by defining capabilities as ”a firm’s capacity

to deploy resources” (Amit & Schoemaker, 1993, p. 35).

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This understanding of the RBV provides a suitable framework for the implemen-

tation and application of MC. It illustrates that such a MC strategy may require far-

reaching changes in the organizational structure (Pine, 1993; Duray, 2002; Piller, 2004;

Rungtusanatham & Salvador, 2008; Salvador et al., 2009). Salvador et al. (2009),

for example, suggest a framework of three capabilities that are necessary in this context.

This framework consists of the capabilities ”solution space development”, ”robust pro-

cess design” and ”choice navigation” (Salvador et al., 2009). Thereby, SSD is concerned

with defining a suitable product offering by deciding which product variants will be of-

fered (Salvador et al., 2009, p. 72). The second capability RPD is mainly targeted at

the manufacturing processes of a company and is intended to ensure that the increase in

product variety that usually results from a customization approach, will not significantly

impair the firm’s operations and supply chain (Pine, 1993; Salvador et al., 2009). Lastly,

the CN capability is concerned with the facilitation of the transaction process between

the firm and its individual customers (Salvador et al., 2009).

Identification of research questions

Despite the availability of an ample amount of literature that discusses success fac-

tors or strategic capabilities for the implementation and application of MC (Fogliatto

et al., 2012), there seems to be a discrepancy between the significant evolvement of mass

customization literature (Fogliatto et al., 2012) and the transformation of theoretical

concepts into successful business practice. This leads to the question, whether there

are certain shortcomings of existing research that hinder firms from applying mass cus-

tomization strategies successfully. In this context, we see two critical issues with regard

to existing MC research.

First, a shortcoming may be seen in the fact that literature in some cases oversim-

plifies the issue of realizing respective business models by regarding mass customization

as ”a [mere] performance ideal” (MacCarthy et al., 2003, p. 289): Tu et al. (2001,

p. 202), for example reduce mass customization to certain manufacturing performance

levels by stating that ”[t]he foundation of mass customization is the ability to achieve

customer responsiveness, cost efficiency, and high-volume production, simultaneously.”

Other studies claim that mass customization indispensably requires the availability of

certain co-design mechanisms (Piller et al., 2005). These examples show how a successful

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implementation and application of MC may erroneously be equated with reaching cer-

tain targets or ideal performance levels in business processes. Salvador et al. (2009)

point out that this understanding of MC as a performance ideal is not helpful for the

practical implementation as this approach simply defines an ”ideal state”, which cannot

possibly be reached, instead of providing practitioners with hints for potential step-by-

step improvements that could truly help firms in the realization process. However, even

though this understanding of MC seems to be problematic with regard to the actual im-

plementation and application of the concept, it seems to be the dominant understanding

in MC literature. This becomes apparent in the results of a literature review that we

conducted in order to provide an overview of existing research on strategic capabilities

for mass customization.

For this purpose, we screened existing literature for studies that used quantitative

empirical methodologies to investigate constructs comparable to at least one of the three

MC capabilities of Salvador et al. (2009) (see Appendix 1). The review shows that

most studies do not take a holistic perspective on MC. Instead most studies rather focus

on a single capability in their research models. Furthermore, it is particularly striking

that all reviewed studies apply a reflective measurement approach. The fact that research

exclusively uses reflective measurement models to assess mass customization constructs

in itself is not very surprising, as the use of reflective measures is widely adopted across

all disciplines of organizational research (Diamantopoulos & Siguaw, 2006). However,

this finding corresponds strongly with the above-mentioned shortcoming of regarding

mass customization as a pure performance ideal. This is due to the nature of reflective

measurement models, which consist of individual items that are caused by the latent

variable (Bollen, 1989, p. 65). Thus, in these cases the causality is directed from the

latent variable (in this case the mass customization capabilities) to the observable effect

indicators (Jarvis et al., 2003). This, in turn, means that the measures merely indicate

changes in the performance level of the latent variable: the more a company has com-

mand of a mass capability, the higher will be the reflective indicator value. However,

this form of measurement may only lead to a statement concerning the performance

level of the respective mass customization capability; it is not capable of explaining how

the respective capability is composed of individual resources. Thus, this form of mea-

surement cannot provide practitioners with an instant feedback on possible deficiencies

of their mass customization approach and does not highlight improvement potentials.

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Second, we believe that existing studies lack a sufficient level of detail with regard

to the managerial activities and organizational routines that are necessary for the imple-

mentation and application of high-variety product offerings for MC. As Salvador et

al. (2009, p. 77) state that ”there is no one best way to mass-customize”, their frame-

work does not provide a detailed action plan for the implementation of mass customiza-

tion, but rather defines an initial guideline for this purpose. This guideline consists of

three capabilities, whereby each capability has a rather broadly defined scope. RPD, for

example, is simply defined as re-organizing a firm’s manufacturing and distribution pro-

cess, so that ”an increased variability in customers’ requirements will not significantly

impair the company’s operations” (Salvador et al., 2009, p. 74). However, this goal

can be achieved by various means, ranging from the use of flexible manufacturing tech-

nologies (Jovane et al., 2003) to the modularization of production processes (Tu et al.,

2004b; Abdelkafi, 2008) or the deployment of a highly flexible workforce (Bhattacharya

et al., 2005). In this context, the framework does not assist firms in the development

and composition of the necessary managerial activities on a more detailed level. Thus,

a second shortcoming can be seen in the fact that the MC framework does not provide

sufficient detail with regard to the individual activities or organizational routines that

are necessary as ”building blocks” for the three strategic capabilities. The underlying

problem becomes apparent, when considering the RBV understanding of resources and

capabilities: as indicated above, there are researchers such as Amit and Schoemaker

(1993), who distinguish between these two terms by defining capabilities as a company’s

overarching ability to put the available resources of the firm to a purposeful use. Simi-

larly, Makadok (2001) states that capabilities enable firms to improve the productivity

of their ordinary resources and Day (1994, p. 38) claims that capabilities are complex

packages of skills and accumulated knowledge. Subsequently, firms need to have a certain

set of resources available, before they can use their strategic capabilities to orchestrate

these activities and utilize these resources in an efficient and purposeful way. Similarly,

Tu et al. (2004a, p.374) claim that ”[m]ost of the existing MC studies do not empha-

size the importance of managerial practices and organizational infrastructure that are

required to support the implementation of technologies that support MC.” Therefore,

firms need to develop the necessary resources in form of managerial activities and orga-

nizational routines, so that they become able to successfully follow the guidelines that,

for example, Salvador et al. (2009) provide with their MC framework.

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In conclusion, we identify two shortcomings of existing MC research. At the same

time, these findings are by no means intended to diminish the importance of existing

research results such as the mass customization framework of Salvador et al. (2009).

Of course, these studies pose a considerable contribution, as they lay the necessary

groundwork for further research in the field by purposefully structuring the requirements

of realizing a successful mass customization business model. However, the existing re-

search might not provide sufficient support for practitioners on the level of resources,

whereby the term resources describes managerial activities and organizational routines

in this context.

A formative measurement index for mass customization

In order to respond to this possible shortcoming of mass customization research, we

develop a formative measurement index for mass customization in this paper, which

will be structured according to the strategic capabilities proposed by Salvador et al.

(2009). For this purpose, we identify a set of managerial activities that in an additive

manner constitute the three capabilities that firms need in order to provide a MC

offering. Thereby, we enrich the existing MC research with detailed insights on the level

of managerial activities and organizational routines. In consequence, the development

of a formative measurement index supports mass customizers on an operational level

by providing a suitable starting point for the implementation or improvement of a MC

business strategy.

The formative approach was chosen over a reflective measurement scale, because

the formative measurement models are characterized as describing different dimensions

or facets of a construct (Bollen & Lennox, 1991; MacKenzie et al., 2005; Diamantopou-

los & Siguaw, 2006). Thereby, such measurement models take a behavioral perspective

of the research object, whereas a reflective approach rather uses a cultural perspective

(Coltman et al., 2008). This means that a formative index consists of a set of activi-

ties that are independent of each other and that cause or form a certain latent variable

in an additive manner (Diamantopoulos, 1999) and thus assigns indicators the role of

predictors, instead of regarding them as predicted variables (Diamantopoulos & Siguaw,

2006). However, it has to be noted that in such a setting no indicator must be omitted,

as the entire conceptual domain of the resulting latent construct can only be captured

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by the group of all relevant indicators (MacKenzie et al., 2005). This is in contrast to

reflective indicators, which represent observed variables that are caused by the latent

variable (Bollen, 1989; MacCallum & Browne, 1993). This demands that the reflective

measurement items of a latent construct are highly correlated and should be interchange-

able, as each of them individually describes the entire conceptual domain of the same

underlying construct (MacKenzie et al., 2005).

By employing a formative measurement index instead of the more established re-

flective scales, we make use of the above-mentioned characteristics of formative mea-

surement items. Thereby, we expect two major outputs, which result directly from the

nature of formative index development and that yield potential solutions for the short-

comings with regard to implementing a MC business strategy that we identified earlier:

first, one major benefit of a formative index development lies within the development

process itself. Diamantopoulos and Winklhofer (2001, p. 271) identify five crit-

ical issues for a successful index construction procedure, including the steps ”content

specification” and ”indicator specification”. During content specification the scope of

the latent construct has to be defined. Thereby, the domain of the latent variable has to

be considered in its full breadth, so that the construct is understood with all its facets

and potential ambiguities (Diamantopoulos & Winklhofer, 2001). This task is tightly

coupled with the aspect of indicator specification, as the causal indicators must cover

the entire scope of the latent construct, as defined earlier during content specification.

This means that the construct has to be thoroughly scrutinized in order to generate

an item pool of all formative indicators that could be considered as relevant aspects of

the resulting construct (Diamantopoulos & Winklhofer, 2001). Subsequently, the index

construction process itself forces the researchers to deeply analyze the latent construct

on a level of its constituent parts and thereby will lead to an improved understanding

of MC capabilities as a composition of certain managerial activities and organizational

routines. Second, the index construction leads to the development of a new measure-

ment tool. This resulting index can be used in future studies to assess the performance

level of MC capabilities of companies from a behavioral perspective. It could be argued

that such an assessment could be conducted with reflective measurement scales as well.

This is indeed correct, but we believe that the formative measurement approach by

focusing on a level of activities allows a more purposeful consultation of MC companies,

because the results of an assessment with formative measures directly identify those

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individual activities that constitute a successful mass customization business strategy

and quantify to what extend the firm has implemented a specific activity or routine. In

consequence, such an approach enables MC companies to evaluate the current state of

their capabilities and to identify problematic aspects that require corrective action.

There are several researchers that voice concerns with regard to certain aspects of

the formative measurement approach (Borsboom et al., 2003; Howell et al., 2007; Wilcox

et al., 2008; Edwards, 2011), for example the identification of structural equation models

(Edwards, 2011). However, besides all the potential fallacies of using formative mea-

surement models, the approach also provides specific benefits that might be of value

in certain settings: Arnett et al. (2003), for example, claim that formative indices

serve well as measures of company performance or as benchmarking tools for the devel-

opment of business strategies. The nature of formative measurement models defining

a latent construct by adding up indicators of its individual facets (Bollen & Lennox,

1991; MacKenzie et al., 2005; Diamantopoulos & Siguaw, 2006) allows a comparably

easy benchmarking of managerial efforts and results in an ”intuitive appeal and poten-

tial practical benefits of formative measurement” (Wilcox et al., 2008, p. 1226). Even

critics of the formative measurement approach, such as Howell et al. (2007) state

that formative measures in the form of indices (Diamantopoulos & Winklhofer, 2001) are

conceptually appropriate in use cases as suggested by Arnett et al. (2003). There-

fore, in this study, we develop a formative measurement index for mass customization

structured along the strategic capabilities of Salvador et al. (2009).

Developing a formative index for mass customization

For the development of the formative measurement index, this paper mainly follows the

guidelines suggested by Diamantopoulos and Winklhofer (2001). This procedure

was selected as it is the most frequently applied approach in management research (e.g.

Bruhn et al., 2008; Cadogan et al., 2008; Coltman et al., 2008; Molina-Castillo et al.,

2013). The original guideline encompasses four steps: the two rather theoretical aspects

of content specification and indicator specification, as well as the empirical evaluation

of indicator collinearity, and external validity. For this paper, these guidelines were

extended to include an approach to assess the substantive validity of the items that

result from indicator specification proposed by Anderson and Gerbing (1991), which

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also has been used in other related studies (e.g. Ulaga & Eggert, 2006). The resulting

five-step-procedure is illustrated in Table 2.1.

Table 2.1: Five steps of formative index development as used in this study

Step 1: Content specification↓

Step 2: Indicator specification↓

Step 3: Assessment of substantive validity↓

Step 4: Assessment of indicator collinearity↓

Step 5: Assessment of external validity

Step 1 & 2: Content and indicator specification for mass customization

capabilities

A strict interpretation of the suggested procedure for developing a formative index re-

quires a detailed specification of the focal construct domain in a first step, before po-

tential items that describe facets of this domain can be derived (Diamantopoulos &

Winklhofer, 2001). However, as we were able to use the existing definitions of the three

mass customization capabilities provided by Salvador et al. (2009) as a starting

point, we opted for a more iterative procedure of combining content and indicator spec-

ification. Therefore, we accumulated a repository of management activities and organi-

zational resources that could potentially serve as indicators for one of the three strategic

capabilities as described in the original definitions. For this purpose, we applied sev-

eral methodological approaches. Initially, we conducted 67 semi-structured interviews

(Yin, 2009; Bogner et al., 2005; Glaser & Laudel, 2010) with experts from 53 companies.

Appendix 2 provides an overview of the respective experts. Additionally, we further

discussed the results of the interviews in a series of workshops, following the example

of Hoffmann et al. (2013). Finally, in order to approve the resulting insights, we

carried out an extensive literature review. Following this methodological approach, we

were able to generate a broad list of managerial activities and organizational resources

for MC.

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This list was then used to reconsider the initial definitions of the three mass cus-

tomization capabilities in an iterative approach, so that the requirements of content

specification could be fully met. The results of this procedure are presented in the

following, structured according to the three capabilities of solution space development,

robust process design and choice navigation.

Solution space development

According to the framework proposed by Salvador et al. (2009), the first step towards

a customization strategy is the development of a so called solution space: high levels

of customer need heterogeneity oftentimes force companies to offer a high number of

product variants. Yet, offering limitless choice is economically unfeasible, thus companies

have to make a choice, clearly defining what they are going to offer and which variants

will be excluded (Salvador et al., 2009). In this context, the interviewed experts recognize

different facets of defining a suitable high-variety product offering from the general

definition of solution space development: in a first step, for example, potential design

constraints need to be identified, so that it can be understood which product variants

could realistically be offered and which product options fall out of the scope of the

firm. In this context, ”[d]esign constraints may be functions of the laws of nature, the

environment in which the product will function, governmental regulations, or corporate

decisions or policies” (Mullens et al., 2005, p. 287). Furthermore, the company has

to identify the customer requirements for the respective product domain in order to

identify the so called ”key value attributes” (MacCarthy et al., 2002); i.e. those product

attributes, along which customer needs diverge (Zhang & Tseng, 2007; Salvador et al.,

2009). For this purpose, firms have to understand the customers’ idiosyncratic needs and

derive a selection of product options that corresponds with the heterogeneous needs of

the customers (Piller, 2004; Salvador et al., 2009). Also, firms have to consider potential

adaptations of their product offering: if the level of fit between an existing solution

space and the heterogeneous customer demand within a certain product domain should

be insufficient, the organization needs to revise, trim or extend the available offering

(Salvador et al., 2009). All these aspects should be part of a comprehensive approach

to solution space development and it can be assumed that each of these facets requires

different management activities and organizational routines.

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In this context, the expert interviews and the literature review reveal a broad band-

width of managerial activities that could potentially support firms in these tasks. With

regard to the recognition of potential design constraints, for example, quality function

deployment (QFD) proves to be a very well documented approach for understanding

technical as well as economical limitations of a product (Chan & Wu, 2002). Further-

more, in order to gain a thorough understanding of all technical aspects of the product,

companies could consider approaches such as patent analysis (Graner & Mißler-Behr,

2012), reverse engineering of existing products, or the morphological box method (Jones

et al., 2009). For the identification of the individual customer needs many different mar-

ket research techniques are available. If companies are trying to gather input directly

from the customers, tools such as customer interviews or surveys (Griffin & Hauser,

1993), customer focus groups (Griffin & Hauser, 1993) or conjoint analysis (Green &

Srinivasan, 1990) could be applied. Especially conjoint analysis seems to be a suitable

methodological approach to capture the heterogeneity of customer needs concerning spe-

cific product attributes (Green et al., 1981; Green & Srinivasan, 1990). However, such

a direct interaction with customers may not be able to reveal latent customer needs.

Therefore, firms might also want to consider methods that stimulate the creativity of

customers such as innovation toolkits (Hippel, 2001; von Hippel & Katz, 2002) or idea

contests (Leimeister et al., 2009). Also, companies could try to identify such latent needs

via observation techniques such as netnography (Kozinets, 2002).

In addition, if there are already product concepts available, the customer accep-

tance towards these concepts could be tested with the help of physical, virtual or rapid

prototyping (Dahan & Hauser, 2002; Engelbrektsson & Soderman, 2004) as well as test

market techniques (Mahajan & Wind, 1992). With regard to testing the fit between an

existing solution space and current customer needs, our research reveals several activities

that could serve as potential controlling mechanisms. One possible approach for this is

the use of proxy variables. Examples for such proxies could be the tracking of customer

purchase behavior, the analysis of sales data or the monitoring of customer complaints

(Narver et al., 2004). Customers need to be enabled to transfer their experiences with

the available product offering to the manufacturer. Possible mechanisms for this transfer

range from simple feedback forms or questionnaires (Franke & Schreier, 2008) to regular

workshops with key customers (Wengler et al., 2006; Caemmerer & Wilson, 2010). An-

other potential activity could be the tracking of the actual customer behavior within the

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customization process, especially in an online context. In this case, click stream data

including the number of hits, the search history or the time spent on a certain website

can be used for this purpose (Bucklin & Sismeiro, 2003; Salvador et al., 2009). Also,

the sales staff plays an important role in this context, as these employees can directly

interact with the customers. Thus, it is essential to link the sales personnel with other

functions of the company via specific routines, so that potential changes or pitfalls can

be immediately reported and communicated throughout the firm (Ryals & Knox, 2001).

Lastly, we identify the need to monitor social trends and technological developments.

For this purpose, companies need to establish corporate foresight routines to identify

potential disruptions of the business environment and to turn them into business op-

portunities (Heger & Rohrbeck, 2012). Examples of such foresight activities are trend

analyses, scenarios or technology roadmaps (Ringland, 2010; Rohrbeck & Gemunden,

2011).

For the purpose of indicator specification, the relatively large number of potential

indicators for solution space development on a level of activities proves to be a valu-

able result, because ”if one omits a critical indicator, one is also omitting part of the

construct” (Molina-Castillo et al., 2013, p. 386). With regard to content specification,

however, the breadth of activities might also be considered as an alarming indicator for

a lack of conciseness in the definition of the scope of the construct at hand. Whereas

Nunnally and Bernstein (1994, p. 484) state that ”breadth of definition is extremely

important to causal indicators”, it also has to be considered that the constructs have to

be defined in a sufficiently concise manner, so that it remains possible to capture the

scope of the construct with reflective items, which will be needed for purposes of test-

ing for external validity (Diamantopoulos & Winklhofer, 2001). Following this notion,

we believe that the result of the indicator specification shows the need for reconsider-

ing the definition of solution space development. A review of the identified activities

reveals that all items can be separated into activities that either aim at defining an

initial solution space before market launch or that are concerned with the adaptation of

an existing product offering. This finding is in accordance with Steiner (2014), who

proposes to extend the existing definition of the solution space development capability

by differentiating between the two sub-categories of initial and adaptive solution space

development in the following. We will adopt this suggestion for the course of this paper.

Thereby, we define initial solution space development (ISSD) as the sum of all product

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management activities that are necessary to define those variants of the new product

that will be made available at market launch. While this may consist of tasks such

as gaining a thorough understanding of all technical aspects of the product and the

relevant customer needs, the development of an initial solution space does not include

any product design activities nor the definition of an underlying product architecture.

Adaptive solution space development (ASSD), on the other hand, is defined as the sum

of all management activities that are concerned with the assessment of the market fit of

the existing solution space and potential changes to this offering. In case the existing

solution space does not show a sufficient fit, the organization needs to revise, trim or ex-

tend the available product assortment to comply with changing customer needs and/or

new technologies. If adaptations have to be made, this could be achieved either with the

introduction of new choice options or with the elimination of underperforming existing

variants (Blecker et al., 2006b,a). Table 2.2 provides detailed definitions of the newly

specified definitions of SSD and its sub-dimensions.

Table 2.2: Definition of solution space development and its sub dimensions

Solution space development refers to the capability of an organization to identify thoseproduct attributes in which customer needs differ most (drivers of variety) and that drive theirperceived value (key-value-attributes) and their willingness to pay. In this process of clearlydefining what the company wants to offer and what not, some product variants may have to beeliminated from the set of options, as they might be in conflict with the economic, technical orlegal circumstances of the organization.

During the initial solution space develop-ment an organization defines the set of cus-tomizable product attributes that will be avail-able at market launch. In order to achieve agood fit of the product offering with the het-erogeneous customer demand, the organizationneeds to gain a thorough understanding of alltechnical aspects of the product and the rele-vant customer needs.

Adaptive solution space development: Anorganization should constantly evaluate the fitof the solution space with the heterogeneous cus-tomer demand. In case the existing solutionspace does not show a sufficient fit, the organi-zation needs to revise, trim or extend the avail-able product assortment to comply with chang-ing customer needs and/or new technologies.

Robust process design

As indicated above, the strategic capability of robust process design targets the issue

of additional production costs that may arise from the increase in product variety (Sal-

vador et al., 2009). In the context of a mass customization business model, production is

most likely facing a considerably higher number of parts, processes, suppliers, retailers

and distribution channels (Piller & Steiner, 2013). Thus, with an increasing level of

variety, the production complexity and the uncertainty in business operations are likely

115

to increase as well (Su et al., 2005). In consequence all parts of the value chain ranging

from raw material procurement to production and eventually to distribution will be

confronted with higher operational cost (Piller, 2004; Su et al., 2005). Furthermore, in-

creases in manufacturing cycle times and shipment lead times are to be expected (Kumar

& Piller, 2006; Yao et al., 2007). Thus, firms need to develop a capability that allows

them to maintain the stability of manufacturing processes and the supply chain in order

to be successful in implementing a high-variety production strategy (Pine et al., 1993).

One way to achieve this goal is the suggested robust process design capability, which

is intended ”to reuse or recombine existing organizational and supply chain resources

[...] to deliver customized solutions with near mass-production efficiency and reliability”

(Salvador et al., 2009, p. 74). Subsequently, it can be stated that a successful mass cus-

tomization business model is characterized by a stable, but still flexible, manufacturing

process that provides a dynamic flow of products (Pine, 1995; Tu et al., 2001; Salvador

et al., 2004).

The results from the expert interviews and the respective literature review indi-

cate that the operational realization of such a strategic capability can manifest itself

in several different activities and organizational routines. A possible starting point for

increasing the robustness of the manufacturing process for high-variety product offerings

is the implementation of process modularity (Salvador et al., 2009). This can be put to

practice by considering manufacturing and supply chain processes as segments that are

necessary in order to realize specific product variants (Pine et al., 1993). In such a mod-

ular setting firms can serve individual customer choices by appropriately recombining

the process segments (Zhang et al., 2003). Also, companies could consider the concept of

postponement or delayed product differentiation in manufacturing or distribution logis-

tics in order to increase process robustness. Delayed product differentiation reorganizes

the supply chain into a generic pre-production phase and a second, customer-specific

phase, in which a product is customized to the customers’ preferences (Yang & Burns,

2003; Yang et al., 2004, 2007). Beyond reorganizations of the manufacturing process

itself, there are many new technologies available that can help to increase the process

flexibility: computer integrated manufacturing (Boyer & Lewis, 2002; Piller, 2004), flex-

ible automation (Tu et al., 2001; Zhang et al., 2003; Koste et al., 2004), robotics (Duray

et al., 2000) or the use of rapid manufacturing technologies such as 3D printing (Tuck

et al., 2008) are some of the examples that the expert interviews reveal in this context.

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Besides these flexible technologies, companies need to train their employees for deal-

ing with novel task, so that they can be assigned flexibly in the manufacturing processes

(Bhattacharya et al., 2005; Salvador et al., 2009). Furthermore, supply chain logistics

need to be reconsidered as well: just-in-time and just-in-sequence logistics enable a lean

logistics concept that allows prompt response times, even in case of unexpected changes

(Blecker et al., 2006b; Aigbedo, 2007). Also, it is of strong importance to already con-

sider the robustness and flexibility of manufacturing processes during the development

of new products and their respective manufacturing processes. This becomes apparent,

when discussing different approaches of developing a range of product variants: hereby,

it has to be decided whether each product variant is developed separately, or whether

the new product development process is carried out jointly for a variety of products.

Research shows that the approach of developing products one by one oftentimes leads

to a proliferation of products and parts, as issues of commonality and standardization

do not receive sufficient consideration (Simpson et al., 2006; ElMaraghy et al., 2013).

Thus, literature recommends a platform-based development approach that builds on an

overall logical structure for ”generating a family of products by providing a generic um-

brella to capture and utilize commonality [, while designing] an entire class of products

[...] based on individually customized requirements within a coherent framework” (Jiao

et al., 2007, p. 6).

If companies decide to follow this approach and intend to develop a full product

offering in a joint development process, the definition of the product architecture has a

major impact on the manufacturability of the future product variants as well (Mikkola,

2007). The selection of a specific product architecture e.g. integrated, modular or

parametric determines the ”mechanism” that is used to reach process flexibility (Her-

mans, 2012). Thus, companies have to decide rather early which product architecture

and which realization mechanism are best suited for the production of the intended

solution space. Lastly, the issues above already clearly indicate that cross-functional

collaboration plays an important role in developing new products and their respective

manufacturing processes. In order to guarantee manufacturability all relevant functional

areas of the company should be integrated in the development processes (De Clercq et al.,

2011). Such a use of cross-functional teams (Song et al., 1998; Yasumoto & Fujimoto,

2005) or concurrent engineering methodology (ElMaraghy et al., 2013) for product and

process development allows the firm to integrate the specific requirements of ramp-up

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management or manufacturing at an early point of time (Du et al., 2003; Gross & Ren-

ner, 2010).

Similar to the results for solution space development, the expert interviews yield

a relatively large number of managerial activities that could potentially serve as causal

indicators for robust process design. It becomes apparent that the impact of increasing

product variety on production cost cannot be mitigated exclusively by reconfiguring the

manufacturing process and bringing a higher degree of flexibility to the production, but

product development has to be considered as well. This might indicate that there are

more facets to robust process design than captured in the original definition of Salvador

et al. (2009).

Again, we believe that this breadth in facets of the focal construct is beneficial for

the purposes of providing guidelines for implementing mass customization business mod-

els, but it might cause problems with regard to capturing the full scope of the construct

domain in the reflective items that are necessary for evaluating the external validity of

the formative index. Thus, with regard to content specification, we suggest to introduce

two sub-categories of robust process design by separating between activities that are con-

cerned directly with the manufacturing process and activities during the development

of new products that can indirectly lead to an improvement of manufacturing process

stability. Following this notion we define the sub-categories ”Process Design for Process

Robustness” and ”Product Design for Process Robustness”. Thereby, with regard to

process robustness (PR) we follow the suggestions of Salvador et al. (2009) and de-

fine it as the capability that aims at achieving the necessary robustness of manufacturing

processes with respect to the heterogeneity of ordered product variants, by determining

a suitable production system architecture and respective logistics that facilitate an ef-

ficient and flexible production. Beyond this aspect, however, we extend the framework

of Salvador et al. (2009) with an additional sub-capability called product design for

process robustness (PDPR), which builds on the idea that the use of certain activities

during new product development can foster the robustness of the resulting manufactur-

ing processes. Similarly, Vickery et al. (1999a) claim that a close collaboration of

product design and development, marketing, and manufacturing is needed in order to

realize sufficient product flexibility. Subsequently, we formulate the following definition:

product design for process robustness aims to achieve the necessary robustness of man-

ufacturing processes with respect to the heterogeneity of ordered product variants, by

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determining a product architecture that facilitates an efficient and flexible production.

Table 2.3 provides detailed definitions of the newly specified definitions of RPD and its

sub-dimensions.

Table 2.3: Definition of robust process design and its sub dimensions

Robust process design refers to the capability of an organization to develop product ar-chitectures and the respective manufacturing processes so that the previously defined solutionspace can be manufactured with near mass production efficiency. That means, successful robustproduct / process design leads to a higher degree of operational flexibility, so that an increasein product variety does neither significantly deteriorate the performance of the organization’sprocesses nor impair product quality.

Process robustness aims to achieve the neces-sary robustness of manufacturing processes withrespect to the heterogeneity of ordered productvariants, by determining a suitable productionsystem architecture and respective logistics thatfacilitate an efficient and flexible production.

Product design for process robustnessaims to achieve the necessary robustness of man-ufacturing processes with respect to the hetero-geneity of ordered product variants, by deter-mining a product architecture that facilitates anefficient and flexible production.

Choice navigation

With regard to the third strategic capability Salvador et al. (2009) claim that firms

need to develop and implement a choice navigation capability. From an economic point

of view, choice navigation addresses the issue of keeping costs associated with the trans-

fer of information between firms and customers as low as possible, while increasing the

overall value for customers at the same time. As mentioned above, customizing products

according to the specific needs of each individual customer requires additional informa-

tion to be transferred between firms and their customers (Wind & Rangaswamy, 2001;

Claycomb et al., 2005; Franke et al., 2009). This information transfer usually takes place

in form of an interactive process, which is inevitably associated with additional transac-

tion costs for the customer and the company (Broekhuizen & Alsem, 2002; Dellaert &

Stremersch, 2005; Merle et al., 2010). For customers, the amount of these non-monetary

costs depends on how they evaluate the interaction process in terms of the effort and

the perceived benefits arising from it. Customer effort is caused by the need to ac-

tively take part in the interaction process. This can lead to dissatisfaction or may even

cause the customer to not buy any product at all (Huffman & Kahn, 1998). Therefore,

companies need to apply certain activities in order to support customers in identifying

suitable solutions, while minimizing complexity and burden of choice (Salvador et al.,

2009). Additionally, literature on customization offerings in B2C markets indicates that

hedonic benefits can be associated with interaction processes: customers expect to have

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a more enjoyable shopping experience while creating their unique solution (Franke &

Piller, 2003; Schreier, 2006; Franke & Schreier, 2008; Franke et al., 2010; Merle et al.,

2010). For firms these interaction processes can cause additional transaction costs in the

internal processing of the individual customer orders and all related customer-specific

information. Thus, it is conceivable that the implementation of a mass customization

business model also might require certain activities or organizational routines that are

related to internal information processing within the respective manufacturing company

(Aydin & Gungor, 2005).

With regard to these considerations of choice navigation, our expert interviews

and literature review lead to the identification of the following managerial activities

and organizational resources as potential causal indicators: in order to reduce variety-

induced complexity, companies should implement an easily understandable (cf. Huffman

& Kahn, 1998; Broekhuizen & Alsem, 2002; Dellaert & Stremersch, 2005; Randall et al.,

2005, 2007; Salvador et al., 2009) and well-structured interaction process (Randall et al.,

2005; Schreier, 2006; Randall et al., 2007). Provision of technological devices, such as

augmented reality devices or 3D-visualizers, can help customers to get a better under-

standing of their own needs (Ninan & Siddique, 2006; Dellaert & Dabholkar, 2009) and

thus can also reduce uncertainties during the customization process. In addition, it

is suggested to provide customers with more information about the product and the

available customization options during the process. Such information is valuable for the

customers, since they cannot test or see the product before it will be manufactured (Huff-

man & Kahn, 1998; Franke & Piller, 2003; Dellaert & Dabholkar, 2009). Randall et

al. (2005) note that different sales channels or differently designed interaction processes

provide the possibility to address different types of consumers more precisely. For online

customization offerings it is recommended to implement configuration or software tools

to interact and co-design with customers (cf. Piller, 2004; Randall et al., 2005, 2007;

Salvador et al., 2009). Other studies suggest complementing online-based customiza-

tion offerings with recommendation systems (Frutos et al., 2004). Also, Dellaert and

Dabholkar (2009) show that interaction with trained sales personnel makes online cus-

tomization offerings less complex and more enjoyable for customers. Similar benefits may

result from interacting with sales representatives in classical retail stores. They can help

customers to better understand product characteristics and to match these with their

own needs more precisely (Crosby et al., 1990). Thus, especially in offline-environments,

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an adequate support of customers requires well-trained sales staff members, who possess

all necessary competencies (Broekhuizen & Alsem, 2002; Randall et al., 2005).

Furthermore, our research also revealed management activities concerning the in-

ternal processing of information: key enabler for the efficient provision and processing of

information are information systems, information technologies and configuration systems

(cf. Piller, 2004; Dietrich et al., 2007; Randall et al., 2007; Fogliatto et al., 2012). These

systems allow serving customers with product related information during the customiza-

tion process in real time (Chang & Chen, 2009; Trentin et al., 2011). This includes the

provision of information for a customer specific product with regard to feasibility (Ninan

& Siddique, 2006), technical characteristics and virtual models (Ninan & Siddique, 2006;

Chang & Chen, 2009; Trentin et al., 2011), price and costs (Dellaert & Dabholkar, 2009;

Franke et al., 2009; Trentin et al., 2011), or delivery dates (Ninan & Siddique, 2006).

However, these systems also allow for an automatic generation of information about

each single product variant, thereby increasing the efficiency of internal operations. For

example, firms could benefit from such systems in the automated preparation of bills

of materials (Aydin & Gungor, 2005; Dietrich et al., 2007; Trentin et al., 2011) or fast

access to manufacturing related information (Trentin et al., 2011). Complementarily,

the availability of technologies such as measurement devices (Zipkin, 2001; Fiore et al.,

2003, 2004; Piller, 2004) and appropriate (technical) sales support systems (Franke &

Piller, 2003; Piller, 2004; Salvador et al., 2004; Randall et al., 2007) can help to collect,

process and use complex information during the interaction process, and thereby may

lead to an increase in process efficiency.

The analysis shows that – in its original form – the definition of choice navigation

is solely dealing with measures that are directed at supporting customers during the

choice or customization process. However, besides managerial activities that serve the

purpose of enabling such a customer-manufacturer-interaction, our exercise reveals a

relatively large number of items that are exclusively related to internal processes within

the customizing firm. Thus, we believe that in order to take all relevant aspects into ac-

count this third capability for mass customization needs to be redefined with a broader

scope. As we believe that this capability goes beyond helping customers to navigate the

existing product offering of a mass customizer, but rather includes all transaction cost

aspects of the customer-manufacturer-interaction, we rename the respective capability

as ”interaction competence” (IC) for the remainder of this paper. Thereby, we define

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interaction competence as a firms’ ability to master all aspects and activities related to

the necessary information transfer during the customization process. In a second step

similar to the content specification procedure for the other two capabilities we define

two sub-dimensions of interaction competence, namely external and internal interaction

competence. Thereby, the construct of external interaction competence (EIC) is very

similar to the original choice navigation construct of Salvador et al.’s (2009) frame-

work. It refers to the ability of a company to efficiently support customers in identifying

their needs and creating their own solutions, such that choice complexity is minimized

and/or enjoyment of the search/configuration process is maximized.

Additionally, internal interaction competence (IIC) will be defined as a companies’

ability to efficiently cope with the flow of all customer-order-specific information. This

ability to internally handle information in an efficient manner is an important prereq-

uisite for the successful realization of interaction processes with customers (Fogliatto

et al., 2012). On the one hand, this includes aspects such as providing customers with

distinct product-related information during interaction processes. The importance of

providing real-time feedback to customers on product configurations, for example, has

been stressed by different studies (cf. Ninan & Siddique, 2006; Tien, 2006; Randall et al.,

2007; Chang & Chen, 2009; Dellaert & Dabholkar, 2009; Franke et al., 2009; Salvador

et al., 2009; Trentin et al., 2011). Providing product-related information helps to increase

transparency of the interaction process, and reduces the risk that customers experience

complexity and uncertainty (Broekhuizen & Alsem, 2002; Dellaert & Stremersch, 2005).

Furthermore, it increases satisfaction and demand for customized products (Chang &

Chen, 2009). Another aspect of internal interaction competence incorporates a com-

panies’ ability to process customer and order-specific information in order to increase

the efficiency of internal operations. Table 2.4 provides detailed definitions of the newly

specified definitions of IC and its sub-dimensions.

Table 2.4: Definition of interaction competence and its sub dimensions

Interaction competence refers to the capability of an organization to compensate the addi-tional search and information costs (transaction costs) that are typically associated with theadoption of a mass customization strategy.

During the external interaction compe-tence includes the ability to support customersin identifying their needs and creating their ownsolutions, such that choice complexity is mini-mized and/or enjoyment of the search/configu-ration process is maximized.

Internal interaction competence incorpo-rates the capability to efficiently handle theflow of inbound (e.g. individual order specifi-cations) and outbound (pricing, delivery dates,etc.) customer-specific information.

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Results of the content and indicator specification steps

As indicated by Diamantopoulos and Winklhofer (2001, p. 271) the first step

in the suggested procedure for constructing a formative index is the so called content

specification. The aim of this step lies within defining the scope of the domain of con-

tent, which the resulting index is supposed to capture (Diamantopoulos & Winklhofer,

2001). For this purpose, the three original strategic capabilities of Salvador et al.

(2009) were further broken down into six constructs. Therefore, the results of the expert

interviews and the literature research as presented above were condensed into six new

definitions that extend the original framework of Salvador et al. (2009) by explicitly

incorporating additional facets of the constructs that were identified during the process.

The reconsideration of the strategic capabilities is visualized in Table 2.5.

Table 2.5: Reconsideration of the strategic capabilities framework

Solution space development (SSD) ⇒ Initial solution space development (ISSD)Adaptive solution space development (ASSD)

Robust process design (RPD) ⇒ Process robustness (PR)Product design for process robustness (PDPR)

Interaction competence (IC) ⇒ Internal interaction competence (IIC)External interaction competence (EIC)

For the further course of the paper the resulting six constructs will be used as

first-order latent variables, whereas each of them incorporates several activities or or-

ganizational routines of the respective capability. This means in terms of dimensional-

ity that the three original capabilities represent multi-dimensional constructs that are

modeled as formative-formative second-order constructs with their respective two sub-

dimensions, which corresponds with type IV according to Jarvis et al. (2003). This

second-order design is chosen, as it allows capturing different aspects of the higher-order

construct and allows breaking down complex formative constructs with many indicators

into different sub-contracts (Becker et al., 2012).

After defining these core constructs of the paper in detail the existing list of potential

formative indicators was refined in order to complete the task of item specification.

Hereby, it is of importance to cover the entire scope of the variable, as it was defined

during content specification (Diamantopoulos & Winklhofer, 2001, p. 271). Even though

Rossiter (2002) generally confirms this statement, he also states that grasping all

relevant components is practically impossible. Hence, it is suggested that during item

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specification, researchers should identify the main components of the construct instead of

searching for every low-incidence component (Rossiter, 2002). Different studies followed

this approach in recent years (e.g. Cadogan et al., 2008; Molina-Castillo et al., 2013).

Therefore, we decided to adopt this approach as well. Nevertheless, we tried to provide

an item pool for each of the six capabilities that is as capacious as possible. As a result

of this item specification procedure, almost 100 managerial activities and organizational

routines could be identified as potential causal indicators. After identification all items

were carefully formulated according to generally accepted guidelines (Spector, 1992) and

the item score was set to a seven point Likert format. The resulting item pool is listed

in Appendix 3.

Step 3: Assessment of substantive validity

In addition to the suggested indicator specification process of Diamantopoulos and

Winklhofer (2001, p. 271), a pre-test exercise following a procedure suggested by An-

derson and Gerbing (1991) was applied in a second step. This test can be employed

in order to evaluate the substantive validity of the identified items, meaning that it can

be established to what extend an item is theoretically linked to a specific construct. For

this purpose, we created an online questionnaire, in which all items appear in a random

order and each one is explained in a one-sentence definition (Anderson & Gerbing, 1991,

p. 735). This questionnaire was then distributed to 60 MC experts from academia and

industry during the Milano Mass Customization Workshop 2013. In this item sort task,

the experts were asked to match each item with the most suitable strategic capability. In

consequence, two indices are proposed in order to measure to what extend the items are

linked to the posited constructs the proportion of substantive agreement and the sub-

stantive validity coefficient. The proportion of substantive agreement can be described

as the ”proportion of respondents who assign an item to its intended construct” and is

defined as the quotient of the number of experts who assigned a measure to its posited

construct to the total number of experts who take part (Anderson & Gerbing, 1991,

p. 734). The substantive-validity coefficient represents the quotient of the difference

between the number of experts assigning an item to its posited construct and the high-

est number of assignments to any other construct, and the total number of respondents

(Anderson & Gerbing, 1991, p. 734). Based on the results of the pre-test exercise, items

with particularly low substantive agreement and substantive validity were eliminated,

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other items were rephrased. This procedure resulted in a refined item pool, which con-

tains 71 items. The full list of items and the assignment of the individual items to the

six latent constructs is available in Appendix 4.

Quantitative survey

For the completion of the remaining steps of the suggested guidelines for developing a

formative index, quantitative survey data is required (Diamantopoulos & Winklhofer,

2001). For this reason, we conducted a large scale survey among European manufac-

turing companies, who are knowledgeable with regard to high-variety production en-

vironments. The study was targeted at senior managers, product managers or other

general managers with a good overall knowledge of the company and its processes. The

managers were interviewed using an online-based, standardized questionnaire, which is

mainly composed of the items that were developed during item specification. Other

questionnaire items were adapted from established scales from literature. Appendix D

provides a full overview of all items used in the online survey. Before the questionnaire

was released to the respondents, a panel of academic experts commented and refined

the questionnaire.

In order to identify potential respondents, two different approaches were adopted.

First, Orbis, a worldwide database of firm information made available by the Dutch

provider Bureau van Dijk was exploited. Therefore, a Boolean search strategy with

multiple selection criteria was applied (search criteria: NACE-code; number of employ-

ees; country; availability of contact data (telephone number)), leading to a total number

of 7.184 companies that could be identified in this first step. This list was then further

refined manually by excluding companies that did not meet a second, more restrictive

set of selection criteria (e.g. focus on business-to-business firms; exclusion of pure service

providers). Ultimately, this procedure resulted in the identification of 3.726 companies

that met the requirements of the study. As there were e-mail-addresses available for

1.626 of these 3.726 companies, these firms were contacted directly via e-mail and the

respective representatives were asked to participate in the online survey. The remaining

2.100 firms were contacted via phone and were asked to identify a suitable representative

for the company. All firm representatives that could be identified in this way were then

asked to participate in the survey via e-mail. All identified contacts received an email

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with a personalized letter as well as a link to the online questionnaire. As an incentive

for participation, a donation to the World Wildlife Fund (WWF) for each completed

questionnaire was promised. All contacts that did not respond to the invitation e-mails,

received a first and, if necessary, a second reminder. Overall, 96 complete responses

(no missing values) from firms of eleven different NACE code divisions could be used

for the analysis, which equals a response rate of 2.6%. Low response rates are not

uncommon for organizational level questionnaires directed toward top management or

high representatives (Koufteros et al., 1998; Baruch, 1999), and previous studies have

exhibit similar low response rates for surveys from high-level respondents (cf. Dwyer &

Welsh, 1985; Nahm et al., 2003; Li et al., 2005; Ettlie & Rosenthal, 2011, response rates

ranging from 2.5% - 7.47%). The small number of responses requires considerable test

for bias: assessment of non-response bias reveals that early and late responses were not

significantly different (Armstrong & Overton, 1977). This test was applied to the demo-

graphic characteristics and all principal constructs. T-Test comparisons between early

and late respondents showed only insignificant differences (p > 0.1). Reasons for non

responding presumably include the high effort in terms of invested time and knowledge

about firm processes, which was necessary for completely answering the questionnaire.

Table 2.6 and Table 2.7 provide a more detailed overview of the sample with regard

to the position of the respondents in the respective firms, as well as the participating

industry sectors. Furthermore, Table 2.8 provides an overview of the distribution of the

countries of origin of the participating firms.

Lastly, the respondents were asked to indicate the predominant degree of customiza-

tion in the business processes of their respective firm, by selecting one of the following five

strategic approaches defined by Lampel and Mintzberg (1996): ”pure standardiza-

tion”, ”segmented standardization”, ”customized standardization”, ”tailored customiza-

tion” and ”pure customization”. As Table 2.9 shows, the respondents are distributed

across all categories, but at the same time almost 75% of the participating firms claim

that customization is a significant aspect of their business model. Subsequently, the

participating firms can be regarded as a suitable sample for the purpose of developing a

formative index for the domain of MC.

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Table 2.6: Distribution of respondents’ positions in the survey sample

Department

Corporate Planning / Controlling / Management 13Manufacturing 35Marketing / Sales 19Product Management 4Purchasing 3Research & Development 16Other 6

Table 2.7: Distribution of industries represented in the survey sample

NACE code devision

10 Manufacturing of food products 114 Manufacturing of wearing apparel 418 Printing and reproduction of recorded media 122 Manufacturing of rubber and plastic products 525 Manufacturing of fabricated metal products, except machinery and equipment 526 Manufacturing of computer, electronic and optical products 1627 Manufacturing of electrical equipment 828 Manufacturing of machinery and equipment 3729 Manufacturing of motor vehicles, trailers and semi-trailers 830 Manufacturing of other transport equipment 531 Manufacturing of furniture 6

Table 2.8: Distribution of countries of origin in the survey sample

Country

Denmark 10Finland 12Italy 17Lithuania 1Netherlands 9Norway 2Sweden 9Turkey 4United Kingdom 24United States 8

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Table 2.9: Classification of the degree of customization according to the categoriesdefined by Lampel & Mintzberg (1996)

Degree of customization

Pure standardization 9%Segmented standardization 19%Customized standardization 43%Tailored customization 17%Pure customization 12%

Assessment of reflective constructs

The guideline of Diamantopoulos and Winklhofer (2001, p. 271) proposes that

two approaches should be carried out in order to assess the external validity of a newly

developed formative index: the use of a so called multiple indicators and multiple causes

model (MIMIC) and multi-factor modeling to test nomological validity. Both approaches

require the integration of additional reflective items for the core constructs as well as

for at least one additional latent construct: first, in case of MIMIC models, reflective

items have to reflect the scope of the respective latent variable in order to enable an

evaluation of the validity of new indices (Joreskog & Goldberger, 1975). However, as

there are no adequate reflective scales available that represent the six capabilities of

mass customization, we developed new reflective scales for this purpose. Thus, for each

of the six capability dimensions a scale of at least three reflective items was established.

These items were deducted from literature and evaluated and refined in a workshop with

experts of the field of mass customization and high-variety production environments (see

Appendix 5).

Second, for the assessment of the nomological validity of an index it is necessary

to test for significant intercorrelations greater than 0 between the index and potential

antecedents, correlates or consequences of the index (MacKenzie et al., 2005). For this

purpose, we borrowed an established reflective scale for market performance from lit-

erature (Homburg et al., 2002). The market performance scale grasps how a company

has performed with respect to customer satisfaction, value to customer, and keeping

current customers relative to competitors. We consider this as a potential consequence

of all capability dimensions and expect that a higher level of each capability, regarded

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in isolation, and its respective sub-dimensions is associated with a higher level of mar-

ket performance. For solution space development, for example, this causality can be

derived directly from the definition of the construct: solution space development means

to delineate a product offering according to the ”key value attributes” of the customers

(MacCarthy et al., 2002, p. 76). This means that only those product attributes should be

incorporated, which offer additional value in the perception of the customers (MacCarthy

et al., 2002, p. 76). Subsequently, a solution space that is developed in consideration of

such product attributes can be expected to cause an increased market performance in

terms of customer satisfaction. We also expect a similar causality between robust pro-

cess design and market performance: successful RPD enables a stable, but still flexible,

manufacturing process that provides a dynamic flow of products (Pine, 1995; Tu et al.,

2001; Salvador et al., 2004). Thereby, such a robust process unlocks the potential to

reduce costs, to improve the quality of the products, and to shorten delivery lead times

and, thus, should have a direct, positive impact on market performance (Tu et al., 2001).

Lastly, as indicated above, choice navigation deals with the issue of nonmonetary costs

in the customer-manufacturer-transaction process. This interactive process is inevitably

associated with transaction costs for the firm and its customers (Broekhuizen & Alsem,

2002; Dellaert & Stremersch, 2005; Merle et al., 2010). In this context, choice navigation

means to support customers in identifying suitable solutions, while minimizing process

complexity and burden of choice (Salvador et al., 2009). Thus, we also expect that

successful choice navigation increases customer satisfaction and loyalty and, thereby,

ultimately impacts market performance in a positive manner.

The inclusion of the above-mentioned reflective scales causes the necessity of assess-

ing the additional constructs and their respective items in terms of their applicability in

the context of the given study. All included reflective scales are multi-item scales, and

therefore require an assessment of reliability and validity on item and construct level.

For this purpose, two important preparatory considerations have to be made: first,

it has to be defined how the constructs and items are used in the following analysis.

For the MIMIC models, for example, the reflective parts of each of the three original

capabilities are modeled as type I second-order latent variables, using the respective

sub-dimensions as lower-order constructs (Becker et al., 2012). This type is most ap-

propriate as the sub-dimensions of the capabilities are theoretically distinguishable but

129

correlated (Becker et al., 2012). Furthermore, these reflective-reflective hierarchical la-

tent variable models are operationalized using the so called repeated indicator approach,

meaning that the higher-order latent construct is specified by all reflective indicators of

the respective lower-order latent variables (Lohmoller, 1989; Becker et al., 2012; Ringle

et al., 2012). Thereby, each lower-order component of the reflective hierarchical latent

variables should have the same number of indicators in order to avoid biases (Ringle

et al., 2012). Second, it has to be decided whether covariance-based or variance-based

structural equation modeling (SEM) should be applied. In this context, it has to be

noted that Kolmogorov-Smirnov tests, visual inspection, and assessing the values of

skewness and kurtosis indicate that not all measurement items in our data meet the

assumption of normal distribution. Furthermore, the Mardia-coefficients for some of the

proposed items indicate that multivariate normality is not given (Tabachnick & Fidell,

2007). Also, our attempt to achieve a normal distribution for these items by following

the procedure for data transformation proposed by Tabachnick and Fidell (2007, p.

86) remained unsuccessful. Therefore, covariance-based SEM, which is commonly used

for confirmatory factor analysis (CFA), must not be applied in this case, as the maxi-

mum likelihood (ML) estimation requires normally distributed data. As an alternative

approach in such a setting, literature suggests variance-based SEM with partial least

squares (PLS) using the software SmartPLS 2.0.M3 (Ringle et al., 2005). PLS does not

require normally distributed data (Fornell & Bookstein, 1982) and is not sensitive to

small sample sizes (Reinartz et al., 2009; Hair et al., 2011), and can thus be applied for

the analysis of our data.

After the completion of these considerations the assessment of the reflective scales

needed for the MIMIC models and the tests for nomological validity can be contin-

ued. For this purpose, all scales are integrated into a single model. This approach also

allows testing the discriminant validity of the proposed six sub-dimensions of the capa-

bilities for mass customization firms. In the following the results of these assessments

are described in detail: after excluding a few indicators due to low loadings, 31 indica-

tors exhibit loadings above .7 and squared multiple correlations well above the required

value of .4, thus indicating a sufficient level of indicator reliability (Bagozzi & Baum-

gartner, 1994). The analysis of internal consistency reliability reveals Cronbach’s alphas

ranging from .70 to .91 and composite reliability values of the latent variables of .85 and

higher, thus meeting or being above the recommended thresholds of .7 and .6 respectively

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(Bagozzi & Yi, 1988; Nunnally & Bernstein, 1994). Overall these results indicate a good

internal consistency reliability of the constructs measures. All values for the average

extracted variance (AVE) are higher than the minimum value of .5, providing evidence

of the convergent validity of the measures. To confirm discriminant validity, the Fornell-

Larcker-criterion is applied, which requires that the AVE for every construct is greater

than the square root of the correlation between the constructs (Fornell & Larcker, 1981).

Whereas, the analysis reveals that discriminant validity is given for all three capabilities

and their sub-dimensions, some of the correlations between higher-order and associated

lower-order constructs exhibit insufficient values. However, according to Hair et al.

(2013) such high correlations between constructs of hierarchical latent variables are not

critical, but are even rather foreseeable. The above-described results of the validity and

reliability assessments are also shown in detail in Table 2.10 and Table 2.11. Overall,

these results confirm the assumption that each of the three strategic capabilities for mass

customization which were proposed by Salvador et al. (2009) is represented by two

distinct sub-dimensions.

Table 2.10: Measurement model results on construct level

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

(1) ISSD .88 0 0 0 0 0 0 0 0 0(2) ASSD .73 .90 0 0 0 0 0 0 0 0(3) SSD .93c .94c .83 0 0 0 0 0 0 0(4) PDPR .47 .35 .44 .90 0 0 0 0 0 0(5) PR .47 .37 .45 .52 .88 0 0 0 0 0(6) RPD .54 .41 .51 .88b .86b .77 0 0 0 0(7) EIC .32 .33 .35 .23 .31 .31 .90 0 0 0(8) IIC .43 .46 .48 .46 .41 .50 .48 .88 0 0(9) IC .44 .46 .49 .40 .42 .47 .86a .86a .77 0(10) Market Performance .43 .50 .50 .42 .34 .44 .31 .43 .43 .88

Notes. The numbers in the diagonal (in bold) are the square root of the AVE.Off-diagonal numbers are the correlations among the latent constructs.a Lower-order components of the higher-order latent variable IC.b Lower-order components of the higher-order latent variable RPD.c Lower-order components of the higher-order latent variable SSD.

131

Table 2.11: Measurement model results on item level

ASSD ISSD SSD PDPR PR RPD EIC IIC IC MP1 SMC CR AVE α

ASSD ref 02 .89 0 0 0 0 0 0 0 0 0 .79 .93 .81 .88ASSD ref 03 .90 0 0 0 0 0 0 0 0 0 .81ASSD ref 04 .91 0 0 0 0 0 0 0 0 0 .83ISSD ref 01 0 .90 0 0 0 0 0 0 0 0 .82 .91 .78 .86ISSD ref 02 0 .89 0 0 0 0 0 0 0 0 .78ISSD ref 03 0 .86 0 0 0 0 0 0 0 0 .73ASSD ref 02 0 0 .83 0 0 0 0 0 0 0 .68 .93 .69 .91ASSD ref 03 0 0 .84 0 0 0 0 0 0 0 .70ASSD ref 04 0 0 .86 0 0 0 0 0 0 0 .73ISSD ref 01 0 0 .80 0 0 0 0 0 0 0 .64ISSD ref 02 0 0 .83 0 0 0 0 0 0 0 .69ISSD ref 03 0 0 .82 0 0 0 0 0 0 0 .67PDPR ref 01 0 0 0 .90 0 0 0 0 0 0 .81 .89 .80 .75PDPR ref 04 0 0 0 .89 0 0 0 0 0 0 .79PR ref 02 0 0 0 0 .86 0 0 0 0 0 .75 .87 .77 .71PR ref 04 0 0 0 0 .90 0 0 0 0 0 .80PDPR ref 01 0 0 0 0 0 .80 0 0 0 0 .64 .86 .60 .77PDPR ref 04 0 0 0 0 0 .77 0 0 0 0 .60PR ref 02 0 0 0 0 0 .71 0 0 0 0 .50PR ref 04 0 0 0 0 0 .80 0 0 0 0 .65EIC ref 02 0 0 0 0 0 0 .91 0 0 0 .83 .90 .81 .77EIC ref 03 0 0 0 0 0 0 .89 0 0 0 .80IIC ref 02 0 0 0 0 0 0 0 .87 0 0 .76 .87 .77 .70IIC ref 03 0 0 0 0 0 0 0 .89 0 0 .78EIC ref 02 0 0 0 0 0 0 0 0 .81 0 .66 .85 .59 .76EIC ref 03 0 0 0 0 0 0 0 0 .74 0 .55IIC ref 02 0 0 0 0 0 0 0 0 .73 0 .53IIC ref 03 0 0 0 0 0 0 0 0 .78 0 .60Mark Perf 01 0 0 0 0 0 0 0 0 0 .92 .84 .91 .78 .86Mark Perf 02 0 0 0 0 0 0 0 0 0 .83 .69Mark Perf 03 0 0 0 0 0 0 0 0 0 .89 .79

Notes. ∗Market performance. SMC = Squared multiple correlation. CR = Composite reliability.AVE = Average variance extracted.

Step 4: Assessment of indicator collinearity

The issue of indicator collinearity has a specific importance with respect to formative in-

dicators (Diamantopoulos & Winklhofer, 2001). Contrary to reflectively measured latent

constructs, where high positive intercorrelations among the indicators are desired (Cron-

bach, 1951; Nunnally & Bernstein, 1994), it is conceptually not conclusive for formative

models to have multiple indicators containing equal information since the construct is de-

fined as a function of multiple different causes (MacKenzie et al., 2005). Thus, a specific

pattern of intercorrelations among the formative indicators is not expected (Coltman

et al., 2008). Bollen and Lennox (1991) note that formative indicators might be

positively correlated, negatively correlated, or uncorrelated with each other. However,

as the relationships between the construct and the indicators is analogous to multi-

ple regression models basing on linear equation systems, high levels of intercorrelations

among the indicators affect the stability of the indicator coefficients (Diamantopoulos

132

& Winklhofer, 2001): validity of formative indicators is reflected in the magnitude and

significance of the path between the indicator and the construct (MacKenzie et al.,

2005). In the case of multicollinearity among indicators, it is difficult to isolate the

distinct impact of the single indicator on the construct (Diamantopoulos & Winklhofer,

2001). Therefore, indicators that can nearly be substituted by a linear combination of

the remaining indicators are inappropriate for remaining in the index (Coltman et al.,

2008) and an exclusion of the respective indicators from the index needs to be consid-

ered (Bollen & Lennox, 1991; Diamantopoulos & Winklhofer, 2001). On the other hand,

excluding indicators has to be executed in a very careful manner, because it is always

associated with the risk of changing the construct itself. Collinearity among indicators

can be assessed by calculating their variance inflation factors (VIF). The VIF-values

for all indicators in our study are below the threshold of 5.0 (Weiber, 2009) except one

indicator (ISSD 12). As this indicator also shows high levels of correlation with other

indicators, we exclude this specific indicator from the further analysis. Correlations and

VIF-values for the three assessed models (one for each strategic capability) are available

in the Tables 2.12, 2.13, and 2.14.

133

Table2.12:

Ind

icato

rco

rrel

ati

on

sin

the

SS

Dm

od

el

VIF

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

(21)

(22)

(23)

(24)

(25)

(26)

(27)

(28)

(1)

ISS

D01

2.82

1(2

)IS

SD

023.

09.5

2∗∗

1(3

)IS

SD

032.

48.5

8∗∗

.60∗∗

1(4

)IS

SD

042.

07.6

4∗∗

.49∗∗

.51∗∗

1(5

)IS

SD

052.

60.6

3∗∗

.57∗∗

.48∗∗

.48∗∗

1(6

)IS

SD

062.

25.7

1∗∗

.47∗∗

.50∗∗

.56∗∗

.66∗∗

1(7

)IS

SD

072.

61.4

1∗∗

.40∗∗

.49∗∗

.31∗∗

.50∗∗

.35∗∗

1(8

)IS

SD

082.

05.4

0∗∗

.28∗∗

.43∗∗

.33∗∗

.53∗∗

.54∗∗

.39∗∗

1(9

)IS

SD

091.

86.4

9∗∗

.45∗∗

.45∗∗

.36∗∗

.47∗∗

.53∗∗

.29∗∗

.60∗∗

1(1

0)IS

SD

104.

06.5

8∗∗

.50∗∗

.52∗∗

.58∗∗

.55∗∗

.58∗∗

.34∗∗

.41∗∗

.33∗∗

1(1

1)IS

SD

112.

64.5

4∗∗

.23∗

.42∗∗

.32∗∗

.17

.37∗∗

.39∗∗

.24∗

.37∗∗

.22∗

1(1

2)IS

SD

125.

05.5

2∗∗

.53∗∗

.53∗∗

.41∗∗

.36∗∗

.46∗∗

.36∗∗

.35∗∗

.45∗∗

.41∗∗

.59∗∗

1(1

3)A

SSD

012.7

7.4

1∗∗

.18

.27∗∗

.44∗∗

.33∗∗

.43∗∗

.38∗∗

.31∗∗

.26∗

.46∗∗

.45∗∗

.45∗∗

1(1

4)A

SS

D02

2.9

6.4

7∗∗

.25∗

.45∗∗

.72∗∗

.27∗∗

.41∗∗

.25∗

.40∗∗

.45∗∗

.44∗∗

.56∗∗

.43∗∗

.53∗∗

1(1

5)A

SS

D03

2.9

0.4

3∗∗

.46∗∗

.41∗∗

.33∗∗

.49∗∗

.33∗∗

.33∗∗

.28∗∗

.36∗∗

.26∗

.38∗∗

.45∗∗

.24∗

.32∗∗

1(1

6)

ASS

D04

3.2

9.2

8∗∗

.27∗∗

.36∗∗

.24∗

.29∗∗

.27∗∗

.38∗∗

.30∗∗

.29∗∗

.26∗

.43∗∗

.30∗∗

.52∗∗

.43∗∗

.45∗∗

1(1

7)A

SSD

052.5

5.3

8∗∗

.19

.29∗∗

.28∗∗

.31∗∗

.31∗∗

.36∗∗

.28∗∗

.29∗∗

.17

.50∗∗

.31∗∗

.51∗∗

.47∗∗

.41∗∗

.82∗∗

1(1

8)A

SSD

061.8

3.0

7.1

6.1

1.0

4-.

02-.

02.2

0∗.1

9.1

5-.

07.2

4∗

.26∗

.15

.14

.18

.15

.20∗

1(1

9)

ASS

D07

2.3

1.3

8∗∗

.33∗∗

.41∗∗

.38∗∗

.35∗∗

.42∗∗

.30∗∗

.37∗∗

.26∗

.38∗∗

.38∗∗

.40∗∗

.49∗∗

.47∗∗

.34∗∗

.49∗∗

.51∗∗

.21∗

1(2

0)

ASS

D08

3.0

6.3

4∗∗

.23∗

.54∗∗

.38∗∗

.22∗

.27∗∗

.32∗∗

.27∗∗

.31∗∗

.36∗∗

.41∗∗

.45∗∗

.50∗∗

.52∗∗

.40∗∗

.50∗∗

.46∗∗

.12

.49∗∗

1(2

1)A

SSD

092.4

0.2

1∗

.27∗∗

.39∗∗

.14

.13

.21∗

.32∗∗

.23∗

.23∗

.25∗

.46∗∗

.46∗∗

.46∗∗

.37∗∗

.26∗

.45∗∗

.36∗∗

.23∗

.44∗∗

.50∗∗

1(2

2)A

SS

D10

3.5

3.3

0∗∗

.18

.28∗∗

.23∗

.20

.32∗∗

.35∗∗

.26∗

.23∗

.17

.52∗∗

.35∗∗

.45∗∗

.46∗∗

.29∗∗

.54∗∗

.52∗∗

.16

.43∗∗

.41∗∗

.49∗∗

1(2

3)

ISSD

ref

01

-.2

4∗

.14

.38∗∗

.26∗∗

.11

.13

.29∗∗

.21∗

.22∗

.23∗

.42∗∗

.20∗

.36∗∗

.47∗∗

.21∗

.50∗∗

.35∗∗

.05

.22∗

.46∗∗

.38∗∗

.57∗∗

1(2

4)IS

SD

ref

02

-.2

0.3

6∗∗

.15

.20

.22∗

.29∗∗

.26∗

.23∗

.36∗∗

.09

.38∗∗

.33∗∗

.38∗∗

.31∗∗

.19

.38∗∗

.25∗

.26∗

.37∗∗

.28∗∗

.46∗∗

.42∗∗

.43∗∗

1(2

5)

ISSD

ref

03

-.3

5∗∗

.31∗∗

.19

.25∗

.29∗∗

.37∗∗

.17

.32∗∗

.35∗∗

.18

.37∗∗

.40∗∗

.48∗∗

.32∗∗

.21∗

.47∗∗

.43∗∗

.32∗∗

.38∗∗

.29∗∗

.36∗∗

.42∗∗

.25∗

.41∗∗

1(2

6)

ASS

Dre

f02

-.2

5∗

.31∗∗

.24∗

.21∗

.33∗∗

.37∗∗

.19

.39∗∗

.33∗∗

.23∗

.18

.36∗∗

.30∗∗

.21∗

.31∗∗

.23∗

.26∗

.32∗∗

.33∗∗

.08

.19

.32∗∗

.04

.09

.34∗∗

1(2

7)

ASS

Dre

f03

-.3

6∗∗

.41∗∗

.28∗∗

.38∗∗

.30∗∗

.38∗∗

.25∗

.30∗∗

.32∗∗

.38∗∗

.23∗

.39∗∗

.27∗∗

.32∗∗

.21∗

.04

.05

.25∗

.21∗

.04

.28∗∗

.36∗∗

.09

.18

.39∗∗

.50∗∗

1(2

8)A

SSD

ref

04-

.40∗∗

.44∗∗

.31∗∗

.33∗∗

.40∗∗

.32∗∗

.25∗

.28∗∗

.30∗∗

.30∗∗

.11

.35∗∗

.14

.10

.18

-.05

.03

.20∗

.23∗

-.03

.12

.06

-.08

.16

.29∗∗

.37∗∗

.50∗∗

1

Not

es.∗∗

p<

.01,∗

p<

.05

(tw

o-ta

iled

test

ed).

134

Table2.13:

Ind

icato

rco

rrel

ati

on

sin

the

RP

Dm

od

el

VIF

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

(21)

(22)

(23)

(24)

(25)

(26)

(27)

(28)

(29)

(30)

(1)

PD

PR

013.

981

(2)

PD

PR

023.

94.5

2∗∗

1(3

)P

DP

R03

3.29

.58∗∗

.60∗∗

1(4

)P

DP

R04

4.32

.64∗∗

.49∗∗

.51∗∗

1(5

)P

DP

R05

3.94

.63∗∗

.57∗∗

.48∗∗

.47∗∗

1(6

)P

DP

R06

3.70

.71∗∗

.47∗∗

.50∗∗

.56∗∗

.66∗∗

1(7

)P

DP

R07

2.23

.41∗∗

.40∗∗

.49∗∗

.31∗∗

.50∗∗

.35∗∗

1(8

)P

DP

R08

2.84

.40∗∗

.28∗∗

.43∗∗

.33∗∗

.53∗∗

.54∗∗

.39∗∗

1(9

)P

DP

R09

2.78

.49∗∗

.45∗∗

.45∗∗

.36∗∗

.47∗∗

.53∗∗

.29∗∗

.60∗∗

1(1

0)

PD

PR

10

3.50

.58∗∗

.50∗∗

.52∗∗

.58∗∗

.55∗∗

.58∗∗

.34∗∗

.41∗∗

.33∗∗

1(1

1)P

R01

3.63

.54∗∗

.23∗

.42∗∗

.32∗∗

.17

.37∗∗

.39∗∗

.24∗

.37∗∗

.22∗

1(1

2)P

R02

3.11

.52∗∗

.53∗∗

.53∗∗

.41∗∗

.36∗∗

.46∗∗

.36∗∗

.35∗∗

.45∗∗

.41∗∗

.59∗∗

1(1

3)P

R03

2.72

.41∗∗

.18

.27∗∗

.44∗∗

.33∗∗

.43∗∗

.38∗∗

.31∗∗

.26∗

.46∗∗

.45∗∗

.45∗∗

1(1

4)P

R04

4.44

.47∗∗

.25∗

.45∗∗

.72∗∗

.27∗∗

.41∗∗

.25∗

.40∗∗

.45∗∗

.44∗∗

.56∗∗

.43∗∗

.53∗∗

1(1

5)P

R05

2.40

.43∗∗

.46∗∗

.41∗∗

.33∗∗

.49∗∗

.33∗∗

.33∗∗

.28∗∗

.36∗∗

.26∗

.38∗∗

.45∗∗

.24∗

.32∗∗

1(1

6)P

R06

4.90

.28∗∗

.27∗∗

.36∗∗

.24∗

.29∗∗

.27∗∗

.38∗∗

.30∗∗

.29∗∗

.26∗

.43∗∗

.30∗∗

.52∗∗

.43∗∗

.45∗∗

1(1

7)P

R07

4.27

.38∗∗

.19

.29∗∗

.28∗∗

.31∗∗

.31∗∗

.35∗∗

.28∗∗

.29∗∗

.17

.50∗∗

.31∗∗

.51∗∗

.47∗∗

.41∗∗

.82∗∗

1(1

8)P

R08

1.66

.07

.16

.11

.04

-.02

-.02

.20∗

.19

.15

-.07

.24∗

.26∗

.15

.14

.18

.15

.20∗

1(1

9)

PR

092.

21.3

8∗∗

.33∗∗

.41∗∗

.38∗∗

.35∗∗

.42∗∗

.30∗∗

.37∗∗

.26∗

.38∗∗

.38

.40∗∗

.49∗∗

.47∗∗

.34∗∗

.49∗∗

.51∗∗

.21∗

1(2

0)

PR

102.

85.3

4∗∗

.23∗

.54∗∗

.38∗∗

.22∗

.27∗∗

.32∗∗

.27∗∗

.31∗∗

.36∗∗

.41∗∗

.45∗∗

.50∗∗

.52∗∗

.40∗∗

.50∗∗

.46∗∗

.12

.49∗∗

1(2

1)

PR

112.

27.2

1∗

.27∗∗

.39∗∗

.14

.13

.21∗

.32∗∗

.23∗

.23∗

.25∗

.46∗∗

.46∗∗

.46∗∗

.37∗∗

.26∗

.45∗∗

.36∗∗

.23∗

.44∗∗

.50∗∗

1(2

2)P

R12

2.67

.30∗∗

.18

.28∗∗

.23∗

.20

.32∗∗

.35∗∗

.26∗

.23∗

.17

.52∗∗

.35∗∗

.45∗∗

.46∗∗

.29∗∗

.54∗∗

.52∗∗

.16

.43∗∗

.41∗∗

.49∗∗

1(2

3)

PR

132.

66.2

4∗

.14

.38∗∗

.26∗∗

.11

.13

.29∗∗

.21∗

.22∗

.23∗

.42∗∗

.20∗

.36∗∗

.47∗∗

.21∗

.50∗∗

.35∗∗

.05

.22∗

.46∗∗

.38∗∗

.57∗∗

1(2

4)P

R14

2.50

.20

.36∗∗

.15

.20

.22∗

.29∗∗

.26∗

.23∗

.36∗∗

.09

.38∗∗

.33∗∗

.38∗∗

.31∗∗

.19

.38∗∗

.25∗

.26∗

.37∗∗

.28∗∗

.46∗∗

.42∗∗

.43∗∗

1(2

5)P

R15

2.25

.35∗∗

.31∗∗

.19

.25∗

.29∗∗

.37∗∗

.17

.32∗∗

.35∗∗

.18

.37∗∗

.40∗∗

.48∗∗

.32∗∗

.21∗

.47∗∗

.43∗∗

.32∗∗

.38∗∗

.29∗∗

.36∗∗

.42∗∗

.25∗

.41∗∗

1(2

6)

PR

161.

81.2

5∗

.31∗∗

.24∗

.21∗

.33∗∗

.37∗∗

.19

.39∗∗

.33∗∗

.23∗

.18

.36∗∗

.30∗∗

.21∗

.31∗∗

.23∗

.26∗

.32∗∗

.33∗∗

.08

.19

.32∗∗

.04

.09

.34∗∗

1(2

7)P

Rre

f02

-.3

6∗∗

.41∗∗

.28∗∗

.38∗∗

.30∗∗

.38∗∗

.25∗

.30∗∗

.32∗∗

.38∗∗

.23∗

.39∗∗

.27∗∗

.32∗∗

.21∗

.04

.05

.25∗

.21∗

.04

.28∗∗

.36

.09

.18

.39∗∗

.50∗∗

1(2

8)P

Rre

f04

-.4

0∗∗

.44∗∗

.31∗∗

.33∗∗

.40∗∗

.32∗∗

.25∗

.28∗∗

.30∗∗

.30∗∗

.11

.35∗∗

.14

.10

.18

-.05

.03

.20∗

.23∗

-.03

.12

.06

-.08

.16

.29∗∗

.37∗∗

.50∗∗

1(2

9)

PR

ref

01-

.45∗∗

.30∗∗

.37∗∗

.27∗∗

.31∗∗

.52∗∗

.21∗

.48∗∗

.49∗∗

.38∗∗

.35∗∗

.39∗∗

.29∗∗

.31∗∗

.17

.21∗

.31∗∗

.26∗

.17

.17

.14

.25∗

.13

.20

.31∗∗

.44∗∗

.38∗∗

.45∗∗

1(3

0)P

Rre

f04

-.2

8∗∗

.31∗∗

.34∗∗

.19

.34∗∗

.39∗∗

.22∗

.38∗∗

.28∗∗

.21∗

.22∗

.35∗∗

.15

.12

.24∗

.25∗

.32∗∗

.21∗

.26∗

.10

.16

.33∗∗

.11

.10

.26∗∗

.42∗∗

.28∗∗

.43∗∗

.61∗∗

1

Note

s.∗∗

p<

.01,∗

p<

.05

(tw

o-ta

iled

test

ed).

135

Table2.14:

Ind

icato

rco

rrel

ati

on

sin

the

ICm

od

el

VIF

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

(21)

(22)

(23)

(24)

(25)

(26)

(27)

(28)

(1)

EIC

012.

521

(2)

EIC

021.

59.1

11

(3)

EIC

032.

27.4

1∗∗

.15

1(4

)E

IC04

1.97

.40∗∗

.13

.30∗∗

1(5

)E

IC05

2.84

.43∗∗

.11

.44∗∗

.37∗∗

1(6

)E

IC06

3.07

.26∗

.10

.41∗∗

-.02

.44∗∗

1(7

)E

IC07

2.24

.47∗∗

.23∗

.27∗∗

.13

.32∗∗

.42∗∗

1(8

)E

IC08

2.97

.50∗∗

.20

.58∗∗

.33∗∗

.58∗∗

.42∗∗

.38∗∗

1(9

)E

IC09

3.05

.54∗∗

.10

.36∗∗

.27∗∗

.53∗∗

.50∗∗

.42∗∗

.60∗∗

1(1

0)E

IC10

2.40

.46∗∗

.22∗

.49∗∗

.43∗∗

.50∗∗

.25∗

.35∗∗

.54∗∗

.37∗∗

1(1

1)E

IC11

2.22

.48∗∗

.16

.28∗∗

.26∗∗

.42∗∗

.28∗∗

.35∗∗

.49∗∗

.41∗∗

.47∗∗

1(1

2)E

IC12

3.79

.62∗∗

.14

.53∗∗

.43∗∗

.55∗∗

.29∗∗

.49∗∗

.65∗∗

.54∗∗

.60∗∗

.55∗∗

1(1

3)E

IC13

3.23

.54∗∗

.31∗∗

.51∗∗

.36∗∗

.42∗∗

.17

.47∗∗

.53∗∗

.48∗∗

.63∗∗

.47∗∗

.67∗∗

1(1

4)E

IC14

2.50

.46∗∗

-.01

.27∗∗

.32∗∗

.45∗∗

.42∗∗

.29∗∗

.47∗∗

.50∗∗

.28∗∗

.39∗∗

.29∗∗

.27∗∗

1(1

5)E

IC15

2.37

.38∗∗

.06

.24∗

.37

.48

.33∗∗

.21∗

.48∗∗

.55∗∗

.32∗∗

.41∗∗

.38∗∗

.29∗∗

.51∗∗

1(1

6)II

C01

2.65

.11

.15

.26∗

.17

.28∗∗

.38∗∗

.13

.28∗∗

.23∗

.22∗

.28∗∗

.22∗

.28∗∗

.25∗

.26∗

1(1

7)II

C02

1.87

.21∗

.22∗

.25∗

.15

.22∗

.18

.03

.19

.23∗

.23∗

.23∗

.27∗∗

.26∗∗

.18

.24∗

.19

1(1

8)II

C03

3.05

.19

.10

.36∗∗

.24∗

.45∗∗

.21∗

.09

.39∗∗

.34∗∗

.35∗∗

.30∗∗

.43∗∗

.34∗∗

.28∗∗

.43∗∗

.60∗∗

.20∗

1(1

9)II

C04

3.42

.35∗∗

.07

.35∗∗

.37∗∗

.30∗∗

.23∗

.28∗∗

.40∗∗

.33∗∗

.46∗∗

.38∗∗

.43∗∗

.46∗∗

.15

.35∗∗

.51∗∗

.16

.58∗∗

1(2

0)II

C05

2.83

.22∗

.11

.31∗∗

.25∗

.45∗∗

.23∗

.15

.36∗∗

.26∗∗

.42∗∗

.22∗

.33∗∗

.36∗∗

.17

.39∗∗

.45∗∗

.29∗∗

.63∗∗

.67∗∗

1(2

1)II

C06

2.97

.12

.32∗∗

.17

.07

.13

.29∗∗

.03

.13

.20∗

.12

.25∗

.11

.15

.17

.08

.51∗∗

.42∗∗

.35∗∗

.31∗∗

.29∗∗

1(2

2)II

C07

2.16

.16

-.01

.20

.06

.17

.21∗

.03

.22∗

.25∗

.17

.30∗∗

.17

.18

.28∗∗

.24∗

.46∗∗

.27∗∗

.46∗∗

.37∗∗

.41∗∗

.62∗∗

1(2

3)II

C08

2.14

.36∗∗

.18

.37∗∗

.28∗∗

.38∗∗

.13

.25∗

.28∗∗

.27∗∗

.37∗∗

.27∗∗

.35∗∗

.48∗∗

.19

.21∗

.38∗∗

.46∗∗

.37∗∗

.45∗∗

.42∗∗

.31∗∗

.31∗∗

1(2

4)II

C09

2.63

.41∗∗

.10

.31∗∗

.11

.48∗∗

.56∗∗

.36∗∗

.44∗∗

.45∗∗

.32∗∗

.40∗∗

.35∗∗

.24∗

.58∗∗

.48∗∗

.34∗∗

.24∗

.44∗∗

.34∗∗

.32∗∗

.29∗∗

.37∗∗

.31∗∗

1(2

5)II

Cre

f02

-.4

0∗∗

.19

.35∗∗

.24∗

.49∗∗

.29∗∗

.24∗

.34∗∗

.31∗∗

.28∗∗

.32∗∗

.47∗∗

.30∗∗

.38∗∗

.39∗∗

.36∗∗

.23∗

.50∗∗

.39∗∗

.50∗∗

.35∗∗

.47∗∗

.30∗∗

.41∗∗

1(2

6)II

Cre

f03

-.3

5∗∗

.21∗

.34∗∗

.30∗∗

.55∗∗

.36∗∗

.25∗

.52∗∗

.39∗∗

.52∗∗

.41∗∗

.52∗∗

.49∗∗

.24∗

.42∗∗

.37∗∗

.24∗

.54∗∗

.58∗∗

.59∗∗

.25∗

.31∗∗

.39∗∗

.38∗∗

.54∗∗

1(2

7)E

ICre

f02

-.1

9-.

01.2

9∗∗

.15

.49∗∗

.41∗∗

.26∗

.41∗∗

.46∗∗

.27∗∗

.38∗∗

.35∗∗

.27∗∗

.31∗∗

.53∗∗

.36∗∗

.19

.43∗∗

.30∗∗

.29∗∗

.20

.27∗∗

.22∗

.31∗∗

.41∗∗

.48∗∗

1(2

8)E

ICre

f03

-.1

1-.

10.1

4.0

7.3

7∗∗

.24∗

.10

.31∗∗

.23∗

.25∗

.13

.32∗∗

.25∗

.24∗

.41∗∗

.38∗∗

-.04

.35∗∗

.20

.23∗

.12

.28∗∗

.04

.25∗

.34∗∗

.34∗∗

.54∗∗

1

Not

es.∗∗

p<

.01,∗

p<

.05

(tw

o-ta

iled

test

ed).

136

Step 5: External validity assessment

Formative indicators do not necessarily share the same content and thus differ in their

relationships with antecedents and consequences of the latent construct (Coltman et al.,

2008). For this reason, an examination of the internal consistency reliability (e.g. Cron-

bach’s alpha) as a marker for the consistency across indicators is not reasonably applica-

ble for formative models (Bagozzi & Baumgartner, 1994). Although different approaches

for assessing the reliability of formative models and indicators have been proposed (e.g.

using the correlation between formative indicators and an alternative measure to assess

a construct or test-retest procedures), none of them can be applied universally for for-

mative measurement models (MacKenzie et al., 2005). In this context, Bollen and

Lennox (1991) point out that the available methods are not appropriate for assessing

reliability of formative constructs. Other researchers even consider reliability assess-

ments unnecessary in this context, due to the very nature of the formative measurement

approach (Rossiter, 2002). However, even if conventional reliability assessments are not

applicable, researchers have to test the assignment of items to constructs and need to

assure that all relevant causing indicators of a latent construct are included (Reinartz

et al., 2004). Therefore, instead of using traditional reliability assessments, Diaman-

topoulos and Winklhofer (2001, p. 272) suggest to test the validity of formatively

measured indicators and constructs. In order to examine validity at the indicator level

and external validity, the estimation of a MIMIC model is suggested. This approach

allows estimating the magnitude and significance of the individual paths from the in-

dicators to their respective latent construct, which may serve as an indicator of item

validity (Bollen, 1989; MacKenzie et al., 2005). Hereby, the magnitude of a path indi-

cates the contribution of the respective indicator to the latent construct. Only those

indicators that contribute significantly should be kept in the index. Other indicators are

candidates for exclusion (Bollen, 1989; Diamantopoulos et al., 2008). However, it has to

be noted that this procedure is applicable only in such cases where multicollinearity can

be ruled out (Diamantopoulos & Winklhofer, 2001). Furthermore, the estimation of a

MIMIC model allows an assessment of the entire set of indicators by means of the model

fit. In case that all paths between indicators and the latent construct are significant,

an acceptable model fit can be interpreted as supporting evidence for the chosen set of

indicators (Diamantopoulos & Winklhofer, 2001).

137

For the same reasons as already mentioned above (i.e. non-normally distributed

data and small sample size), a PLS based structural equation modeling approach was

chosen over covariance-based SEM with maximum likelihood estimation. The MIMIC

models were assessed using SmartPLS 2.0.M3 (sample size: 96; 500 samples). In this

study, three MIMIC models were specified, one each for solution space development,

robust process design, and interaction competence with their respective sub-dimensions.

Thereby, the individual MIMIC models each consist of a formative and a reflective

part. Concerning the formative part, recommendations for the modeling of formative-

formative hierarchical latent variable models are still missing (cf. Becker et al., 2012).

For this reason, we follow the suggestion of Becker et al. (Becker et al., 2012), who

claim that the repeated indicator approach to is the most appropriate for these types of

hierarchical latent constructs. With regard to the reflective part of the MIMIC models,

the above-mentioned reflective measurement constructs can be used. These reflective

constructs have already been assessed in terms of validity and reliability. The respective

results indicate that the reflectively formed second-order constructs represent adequate

reference points to assess the external validity of the three MIMIC-models.

For the formative parts of the three MIMIC models the estimation of the PLS mod-

els reveals that several formative indicator weights are not significant. This suggests

that some indicators need to be excluded from the respective model. We address this

issue by following the iterative procedure recommended by Bollen (1989) for each

MIMIC model. Therefore, in each iteration step the formative indicator with the small-

est non-significant value is excluded from the model, followed by a re-estimation of the

remaining model. This procedure is repeated until only formative indicators with sig-

nificant weights are represented in the models. As a result of this approach, the model

designated to evaluate the indicator set for initial and adaptive solution space devel-

opment contains nine indicators, five for ISSD and four for the ASSD sub-dimension

(ISSD: ISSD 3, ISSD 4, ISSD 5, ISSD 8, ISSD 10; ASSD: ASSD 4, ASSD 5, ASSD 7,

ASSD 8). The exclusion procedure for the model for process robustness and product

design for process robustness leads to an indicator structure with three indicators for the

product design sub-dimension and three indicators in the process design sub-dimension

(PDPR: PDPR 1, PDPR 2, PDPR 08; PR: PR 02, PR 15, PR 16). The revised MIMIC

model for interaction competence comprises seven indicators, three indicators for IIC

and four indicators for EIC (EIC: EIC 05, EIC 06, EIC 12, EIC 15; IIC: IIC 01, IIC 05,

138

IIC 09). As it can be seen in Table 2.15, all remaining and significant indicator weights

of the lower-order and higher-order latent formative constructs are larger than .1 and

can therefore be considered relevant according to Seltin and Keeves (1994). Further-

more, the estimations reveal for each model that all path coefficients within the reflective

and formative hierarchical latent constructs are significant (p ≤ .1).

The PLS results for the three MIMIC models provide proof for external validity

of the proposed indices: the estimations reveal for each model that all paths from the

formative hierarchical latent construct to the reflective hierarchical latent construct are

significant (p ≤ .01). The values for the variance explained (R2) for the three reflec-

tive higher-order latent constructs range from .478 to .591, which indicates that the

formative indices cover the scope of the respective construct sufficiently (Chin, 1998;

Diamantopoulos & Siguaw, 2006). The respective calculation results can be seen in

Table 2.16.

Finally, in order to assess the validity of the proposed formative indices in a nomolog-

ical network, the intercorrelations between the formative hierarchical latent constructs

and the hypothesized antecedents, correlates, or consequences can be assessed (MacKen-

zie et al., 2005). Thereby, significant intercorrelations greater than 0 between the latent

constructs indicate a good nomological validity. This validation procedure is particularly

relevant in our case, as some of the initially suggested indicators have been eliminated

from the index (Diamantopoulos & Winklhofer, 2001). Again, SmartPLS was used to

assess the nomological validity of the new indices. Therefore, three multi-factor models

were specified one for each capability and its respective sub-dimensions. Results of the

PLS estimations reveal that the expected effects of the capability constructs on market

performance could be confirmed: we find that the formative measurements for interac-

tion competence (β = .453; t = 5.724), robust process design (β = .469; t = 5.556), and

solution space development (β = .429; t = 4.690) all have strong significant and positive

effects on market performance.

As goodness-of-fit-indicators that are typically used in the context of covariance-

based structural equation modeling are not suitable for variance-based SEM, the main

criterion for the assessment of such structural models is the explained variance R2

(Henseler et al., 2012). In case of the assessment of the nomological validity for the

three newly developed indices the respective R2 values range from .206 to .257 for our

139

Table 2.15: Indicator contribution to the formative hierarchical latent constructs

Variable IndicatorWeight

StandardError

t-value1

ASSD 04 .34 .09 3.69ASSD 05 .19 .13 1.65ASSD 07 .32 .14 2.36ASSD 08 .41 .11 3.82ISSD 03 .20 .12 1.74ISSD 04 .24 .10 2.40ISSD 05 .27 .12 2.28ISSD 08 .34 .10 3.50ISSD 10 .35 .12 3.05ASSD 04 (repeated) .14 .06 2.09ASSD 05 (repeated) .13 .08 1.68ASSD 07 (repeated) .21 .08 2.52ASSD 08 (repeated) .24 .07 3.54ISSD 03 (repeated) .13 .06 1.99ISSD 04 (repeated) .11 .05 2.35ISSD 05 (repeated) .13 .06 2.06ISSD 08 (repeated) .17 .06 2.70ISSD 10 (repeated) .19 .07 2.74PDPR 01 .33 .17 1.93PDPR 02 .52 .13 4.00PDPR 08 .44 .16 2.83PR 02 .62 .11 5.64PR 15 .19 .10 1.86PR 16 .46 .12 4.05PDPR 01 (repeated) .19 .10 1.90PDPR 02 (repeated) .26 .08 3.40PDPR 08 (repeated) .25 .09 2.66PR 02 (repeated) .29 .07 4.10PR 15 (repeated) .11 .06 1.66PR 16 (repeated) .31 .07 4.42EIC 05 .31 .12 2.56EIC 06 .34 .11 3.07EIC 12 .23 .10 2.37EIC 15 .43 .13 3.43IIC 01 .19 .13 1.66IIC 05 .43 .15 2.88IIC 09 .67 .11 6.21EIC 05 (repeated) .19 .08 2.42EIC 06 (repeated) .17 .06 2.85EIC 12 (repeated) .16 .07 2.47EIC 15 (repeated) .27 .08 3.63IIC 01 (repeated) .13 .07 1.96IIC 05 (repeated) .24 .09 2.63IIC 09 (repeated) .28 .06 4.63

Notes. 1derived from the bootstrapping procedure with 96 casesand 500 samples. Critical t-value (10% significance) = 1.65.

140

Table 2.16: PLS estimation for the MIMIC models

Path Path Standard T-value1 R2

coefficient error

ASSD → SSD form .56 .04 13.03ISSD → SSD form .51 .05 10.96SSD form → SSD ref .74 .05 13.80SSD ref → ASSD ref .94 .02 62.34SSD ref → ISSD ref .93 .02 58.63

for SSD ref = .55PDPR form → RPD form .54 .04 12.30PR form → RPD form .56 .05 12.07RPD form → RPD ref .69 .05 12.70RPD ref → PDPR ref .88 .03 33.65RPD ref → PR ref .87 .03 28.09

for RPD ref = .48EIC → IC form .60 .04 15.46IIC → IC form .49 .04 12.10IC form → IC ref .77 .04 18.92IC ref → EIC ref .86 .03 29.92IC ref → IIC ref .86 .03 30.55

for IC ref = .59

Notes. 1derived from a bootstraping procdeure with 96 cases and 500 samples.Critical t-value (10% significance) = 1.65 (two-tailed tests)

target construct (market performance) and are, thus, acceptable. These results are also

supported by the Q2 values of the predictive validity (Geisser, 1974; Stone, 1974). In

order to obtain the Q2 values for market performance, we ran a blindfolding procedure,

which provided values well above zero, thereby indicating the predictive relevance of

our models (Chin, 1998; Henseler et al., 2009). Thus, we received satisfactory values

providing evidence in support of the validity of the hierarchical latent constructs for the

three capabilities (see Table 2.17). Additionally, we integrated all three strategic ca-

pability second-order constructs into one model with market performance as the target

construct in order to investigate the change in the predictive relevance of the model.

Results show that R2 and Q2 of the target construct have improved in moving from

single second-order capability models to a general model which includes all capabilities.

This indicates for the market performance construct that each second-order capability

is relevant for its predictive relevance among the other two second-order constructs.

141

Table 2.17: Results for the multi-factor models

Path coefficient T-Value R2 Q2

Solution Space Development .51 6.44 .26 .18Robust Process Design .47 7.21 .22 .15Interaction Competence .45 6.18 .21 .13

Solution Space Development .28 2.63.34 .26Robust Process Design .21 2.02

Interaction Competence .22 1.98

Notes. Target construct: company performance. R2 = explained variance.Q2 = Stone-Geisser-criterion. Critical t-value (10% significance) = 1.65(two-tailed tests). T-values derived from the bootstrapping procedurewith 96 cases and 500 samples.

Resulting Formative Measurement Indices

Table 2.18 shows the resulting indices for the mass customization capabilities resulting

from the index development procedure described above. In total 22 items remain in use,

whereby each capability sub-dimension is represented by three to five indicators. The

displayed indicator weights are normalized values of the originally calculated weights

shown in Table 2.16. These weights can be used in future studies in the field of mass

customization.

Table 2.18: Resulting formative measurement indices

Initial solution space development (ISSD) FactorWeights1

ISSD 03: We apply customer interviews to identify the customers’ key-value-attributes.

.145

ISSD 04: We apply the lead user method to identify the customers’key-value-attributes.

.173

ISSD 05: We apply customer surveys to identify the customers’ key-value-attributes.

.191

ISSD 08: We apply virtual prototypes to evaluate our customers’ ac-ceptance towards product concepts.

.240

ISSD 10: We apply benchmarking to learn from our competitors whendefining our product offering.

.252

Adaptive solution space development (ASSD)

ASSD 04: We apply trend analysis in order to monitor changes in cus-tomer needs.

.272


142


ASSD 05: We have routines / processes for our sales staff to reportchanges in customer needs that they have observed.

.150

ASSD 07: We analyze sales and revenue data of all product variants inorder to identify poorly performing variants.

.253

ASSD 08: We analyze customer behavior in the configuration processin order to identify potential pitfalls or shortcomings of ouroffering.

.325

Product design for process robustness (PDPR)

PDPR 01: We apply interdisciplinary teams for the development of newproducts.

.253

PDPR 02: We employ simultaneous / concurrent engineering for thedevelopment of new products.

.404

PDPR 08: During the development of new products we give particularattention to manufacturing requirements.

.343

Process robustness (PR)

PR 02: We apply simultaneous / concurrent engineering for the de-velopment of new manufacturing processes.

.486

PR 15: Our manufacturing processes can be reconfigured in a mod-ular way.

.149

PR 16: Our employees are trained so that they can be assigned flex-ibly within the manufacturing process.

.365

External interaction competence (EIC)

EIC 05: During the customization process, we offer extensive infor-mation about all product configuration options.

.236

EIC 06: Customers can receive support from trained employees any-time throughout the entire product customization process.

.258

EIC 12: During the design of our customization process, we paid par-ticular attention to creating an easily understandable pro-cess for our customers.

.178

EIC 15: We help our customers to get a better technical understand-ing of our products by providing information about the in-terrelations between choice options.

.328

Internal interaction competence (IIC)

IIC 01: We apply mechanisms / routines that allow us to track thestatus of customer orders.

.149

IIC 05: We apply mechanisms / routines that allow us to pro-vide real-time feedback concerning product prices anytimethroughout the entire customization process.

.335

IIC 09: We provide technical support systems to assist our salesstaff.

.516

Notes. 1Factor weights are normalized.

143

Discussion

In conclusion of this study, we will outline the contributions of our research, derive

theoretical and managerial implications, discuss limitations of the chosen methodological

approach, and provide an outlook of potential future research directions.

Contributions

The strategic capabilities framework for mass customization companies proposed by

Salvador et al. (2009) suggest three capability dimensions necessary to run a mass

customization business successfully. However, in their study the authors remain on a

rather conceptual stage by describing these capabilities on an abstract level only. Fur-

thermore, a comprehensive operationalization of these strategic capabilities has been

still missing. With this study, we address these research gaps by conceptualizing the

strategic capabilities in a more detailed way as well as by developing formative indices

to provide metrics for the capabilities, following the scale development and evaluation

procedure proposed by Diamantopoulos and Winklhofer (2001). We chose a for-

mative measurement approach over a reflective one as literature gives reason to assume

that the strategic capabilities are based on the sum of different managerial activities and

organizational resources, and thus a formative approach is the most appropriate one.

In order to define the three strategic capabilities proposed in the framework of Sal-

vador et al. (2009) in a more detailed way, we conducted numerous expert interviews,

aiming at identifying all relevant facets of these capabilities. Furthermore, the resulting

insights were refined and supplemented in several workshops and an extensive literature

review. Overall, we thereby identified more than 100 activities and resources that relate

to these capabilities. Subsequently, due to the large number of identified activities and

resources, we discriminate each of the three capability-dimensions of the initial frame-

work into two sub-dimensions. Each of the six sub-dimensions addresses one important

aspect relevant for realizing a mass customization strategy successfully. Basing on the

list of activities and resources, we deduced new definitions for the required strategic

capabilities for MC.

In a sequential step, we aimed at bringing the list of managerial activities and

organizational resources into accordance with the newly derived definitions of the six

144

MC capabilities. Therefore, we conducted a matching exercise during an additional

expert workshop, in which we asked the participants to assign the individual items to

the available capability definitions. Based on this exercise we are able to provide the

first comprehensive conceptualization of the strategic capabilities framework for mass

customization, linking individual managerial activities and organizational resources with

the capability dimensions.

Lastly, and most importantly, an additional contribution of this study is the de-

velopment of the three formative measurement indices for the strategic capabilities for

MC. For this task, we collected quantitative data from 96 European manufacturing com-

panies from different industries with the help of an online survey. With this data set

we were able to carry out an index development procedure and provide support for the

three formative-formative second-order constructs for the strategic capabilities that were

conceptualized. In conclusion, this study provides a well-founded measurement instru-

ment for the strategic capability framework for MC, based on managerial activities and

organizational resources.

Theoretical and managerial implications

The results of this study contribute to the existing research in the field of mass cus-

tomization and have two major theoretical implications. First, the provided comprehen-

sive outline of different managerial activities and organizational resources, which build

the foundation of the strategic capabilities for mass customization business models, as

well as the systematic conceptualization of the six capability-dimensions help to improve

our understanding of why some companies succeed and others fail in implementing and

applying such business strategies. This refined theoretical understanding may serve

researchers as a starting point for the deduction of further research questions in this

domain and may help to extend the scope of MC theory in future.

Second, the most important contribution of our study lies within the development

of a formative measurement index in the context of the strategic capability framework of

MC. We provide researchers with formative metrics for mass customization capabilities,

which enable a comprehensive comparison of companies in terms of their MC capabilities

and their potential to exploit a MC strategy successfully. This means that the presented

indices can be used in future research studies in the field of mass customization and

145

could be applied as formative measurement items in surveys. This poses a significant

contribution to research methodology for two reasons: on the one hand, to the best of

our knowledge, existing studies exclusively use reflective measurement approaches for

MC and therefore are not capable to reflect all facets of the respective capabilities in

such a detailed manner. On the other hand, as discussed above, most available studies

focus on single aspects or capabilities of MC and are thus not able to take potential

complementary effects between the capabilities into account. As our study provides

metrics for all the capability dimensions for MC that have been identified by experts and

in literature, we contribute to MC research by enabling comprehensive studies including

all of these dimensions simultaneously.

Besides these implications for researchers, the results of this study also provide

relevant insights for managers and companies two of which will be discussed in the

following. First of all, practitioners can directly use the indices in their presented form

in a variety of ways. For example, the indices can be applied for benchmarking a firm’s

mass customization performance against the performance of a competitor. Therefore,

all 22 items need to be answered on a 7-point Likert-type scale for each company that

should be included in the benchmarking. The scores can then be weighted based on

the normalized factor weights provided in Table 2.18. Alternatively, the scores of the

22 items can also be averaged to receive a benchmarking result (Arnett et al., 2003).

In order to construct the benchmark ranking, the resulting scores have to be compared:

differences in the resulting index scores indicate a potential advantage or disadvantage of

a participating firm. Additionally, the mass customization capabilities indices can also

be applied to measure the effectiveness of certain management efforts. By measuring a

firm’s mass customization capabilities indices before and after implementing a specific

management strategy or tool, the company can immediately gain an understanding of

the implementation success of its management effort.

With regard to a second managerial implication, it has to be noted that not all

of the originally conceived formative indicators remain in the resulting indices due to

non-significant indicator weights or multicollinearity issues. As we asked companies

from different industries for this study, this result was expectable rather than surpris-

ing. It seems to be obvious that the implementation and application of MC strategies

in different contexts, in terms of product categories or degrees of product complexity,

also requires different sets of managerial activities and organizational resources. For

146

example, we expect that modular product architectures and platform architectures are

more relevant for complex products in industries such as automotive, machinery and

mechanical engineering, whereas these architectures seem to be less relevant for the con-

struction industry for windows and furniture, where principles of parametric design are

frequently applied. Another example is the application of prototypes in order to eval-

uate customers’ acceptance towards a new product concept: this approach seems to be

high relevant for providers of control technology for whom human-machine interfaces are

critical. On the other hand, prototyping is less relevant (and even too expensive) in ma-

chinery and plant construction. In terms of external interaction competence, we expect

that software tools and configurators are more commonly used for less complex products

such as machinery components, rather than for complex manufacturing equipment. In

such industries trained sales stuff usually fulfills the task of helping customers to find

a suitable product. These arguments lead us to the conclusion that the formative indi-

cators composing our indices represent the core activities and resources for companies

pursuing a mass customization business models across all industries. We expect that

each industry (and even each company) has a specific pattern of required managerial

activities and organizational resources compounding the strategic capabilities required.

Thus, beside the core activities and resources composing the indices, we assume that

the complete outline of items proposed in this study is of value for mass customization

companies. Practitioners can go back to the full list of items and can use this list to

identify further company-specific activities that must be implemented and resources that

need to be acquired in order to improve their firm’s mass customization strategy.

Limitations and further research

Naturally, the methodological approach of developing a formative measurement index

is not free of limitations. There are several researchers that voice concerns with re-

gard to certain aspects of the formative measurement approach (Borsboom et al., 2003;

Howell et al., 2007; Wilcox et al., 2008; Edwards, 2011). Edwards (2011), for exam-

ple, provides a resourceful overview of potential criticisms by describing ”six fallacies of

formative measurement”, which are dimensionality, internal consistency, identification,

measurement error, construct validity, and causality. However, it has to be emphasized

that some of these concerns are mainly directed at deciding whether an existing set of

survey items should be used in a formative or a reflective manner. With regard to the

147

issue of internal consistency, for example, it is stated that researchers may take a low

level of internal consistency as a justification for the use of formative measures (Bollen

& Lennox, 1991; Edwards, 2011). Even though this procedure is erroneous and cannot

be accepted, we believe that this criticism of formative measurement approaches is not

relevant in the context of developing a new formative measurement index. Nevertheless,

there are certain concerns such as the issue of identification, for instance that cannot be

neglected. A formative measurement model is not identified (Edwards, 2011) and results

in a latent construct that is ”psychologically uninterpretable” (Bollen & Lennox, 1991,

p.312). This issue can be remedied, as identification can be achieved by incorporating

at least two external, reflective criteria that are caused by the latent construct at hand

(MacCallum & Browne, 1993). Still, the meaning of the latent construct that comes into

being by adding reflective indicators is conceptually ambiguous, because it ”is as much

a function of the dependent variable as it is a function of its indicators” (Heise, 1972,

p.160). Subsequently, depending on the choice of reflective indicators, the meaning of

the latent construct might change (Edwards, 2011) and thus, the benefits of using a

formative measurement models in the context of structural equation models might be

limited. Of course, these concerns need to be taken into account by researchers that

apply such indices in their studies. However, as mentioned above, a formative measure-

ment index still has to be regarded as a valid methodological approach for benchmark

studies (Arnett et al., 2003) and the analysis of latent constructs from a behavioral

perspective (Coltman et al., 2008).

Besides these more general concerns against the formative approach, this study also

contains some limitations that suggest grounds for further research. In the following, we

will exemplarily discuss three concerns that we deem particularly relevant. First, as for

all new measurement instruments, whether reflective or formative, the results need to be

replicated on a second set of data in order to cross-validate the newly constructed mea-

surement instrument (Cudeck & Browne, 1983; Diamantopoulos & Winklhofer, 2001).

Furthermore, it would be desirable to investigate if any industry-specific differences

consist in terms of the relevance of managerial activities and organizational routines,

as already mentioned above. Therefore, the conducted index development procedure

should be replicated with a new data set. Additionally, this should not only be done

with a cross-industry sample, but also with data from individual industries.

148

Second, although we took great care during the identification of relevant manage-

rial activities and organizational resources, we cannot guarantee that we identified all

of them. Furthermore, it could be expected that new management activities and or-

ganizational resources (technologies in particular) evolve over time. For this reason, it

might become necessary to revise the indices, in order to calculate new factor weights

that take new technologies and tools into account and thereby ensure that the indices

remain valid measurement instruments.

Third, another limitation arises from the use of variance-based SEM, which is as-

sociated with some disadvantages in comparison to covariance-based SEM: there is no

established global goodness-of-fit criterion for PLS available. Thus, a test of the overall

model fit is not feasible. Furthermore, variance-based SEM assumes that latent con-

structs with formative indicators are error-free measured (Edwards & Bagozzi, 2000),

whereas in covariance-based SEM, formative latent constructs can be specified with an

error term in order to capture unobserved aspects of the construct (Diamantopoulos &

Winklhofer, 2001; Jarvis et al., 2003). This could lead to biased estimates and results.

Beyond these limitations, there are several possible directions to further extend the

research on the strategic capabilities framework: first, it would be worthwhile to inves-

tigate how the six capability dimensions are related to one another. Harzer (2013)

already conducted a study that investigates the interrelations between the three capa-

bilities suggested in the original framework of Salvador (2009). However, this study

investigates these interrelations from a more cultural perspective, using a reflective mea-

surement approach. Furthermore, the study exclusively includes B2C-companies with

an online product offering. Against this background, a replication and extension of this

study using our newly developed formative measurement approach with a B2B industry

sample could pose a valuable contribution to research. Second, it would be of interest to

examine how these capabilities affect a company’s performance and whether the capa-

bilities are complementary in terms of their impact on performance (Wernerfelt, 1984;

Newbert, 2008). Third, possible influences of contingency factors moderating the impact

of the capabilities on performance could be investigated. Fourth, we identified a research

gap in terms of solution space development: only very few studies investigate the im-

portance of a pre-product-launch and post-product-launch solution space development

activities and their relationship to product and firm performance. As solution space

development marks a critical capability, future studies might provide valuable insights.

149

Appendix

Appendix 1: Overview of quantitative empirical studies on MC capabilities

Paper CN SSD RPD

Ahlstrom and Westbrook (1999) X XChung (2005) XDellaert and Stremersch (2005) XDuray et al. (2000) XDuray (2002) XDuray (2004) XFiore et al. (2004) XFranke and Piller (2004) XGruner and Homberg (2000) XHuang et al. (2008) XHuang et al. (2010) XHuffman and Kahn (1998) XKamali and Loker (2002) XKotha (1995) XLiechty et al. (2001) XLiu et al. (2006) XLiu et al. (2010) XLiu and Deitz (2011) XLiu et al. (2012) X XMeuter et al. (2000) XOon and Khalid (2003) XPiller and Schoder (1999) XSalvador et al. (2008) X X XSchreier (2006) XSquire et al. (2006) XTu et al. (2001) XTu et al. (2004a) XTu et al. (2004b) Xvan Hoek (2000) XVickery et al. (1999b) XYen and Ng (2007) X

Results 13 4 18

Notes. CN = Choice navigation. RPD = Robust process design.SSD = Solution space development.

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Appendix 2: Overview of semi-structured expert interviews

Company Position in Company Industry

Adidas Category Manager miadidas Apparel & ShoesAtom Group Head of Research & Innovation ConsultancyAerogistics CEO Airplane ComponentsBene Head of Engineering Office furnitureBimatec-Soraluce Technical Director Milling & Boring MachinesBimatec-Soraluce Engineer Milling & Boring MachinesBivolino COO & Co-Founder Apparel & ShoesBuild-A-Bear PR Manager ToysBuild-A-Bear Managing Director ToysChocri CEO FoodChocri CEO USA FoodClaas Product Manager Agricultural MachineryClaas System Engineer Agricultural MachineryCorpus.e CEO Body Scan TechnologyCustomax CEO E-business SoftwareCyledge CEO SoftwareDaimler Cars Project Manager AutomotiveDaimler Trucks & Manager ITC/BP AutomotiveBusesDeinBonbon CEO FoodErtl/Renz Key Account Manager Sport Equipment &

ShoesFESTO Key Account Manager Automation TechnologyFESTO Key Account Manager Automation TechnologyFESTO Head of Modular Products Ser-

viceAutomation Technology

FESTO Head of Intelligent Components Automation TechnologyFESTO Head of Sales Qualification &

Product MarketingAutomation Technology

FESTO Head of Product ManagementValves Automation Technology

FESTO Head of Product Center Air Sup-ply

Automation Technology

FESTO Head of Project Office GlobalProduction

Automation Technology

Ford Planning & Budget Coord.,Global

Automotive

Vehicle DynamicsFord Manager R&D Europe AutomotiveHenkel Material Group Leader at Global

PurchasingFMCG

Johnson Controls Specialist New Technologies AutomotiveKarl-Otto-Braun Head of R&D Medical Textiles


151


KERNenergie Co-Founder FoodKrone Product Manager AutomotiveKrone Quality Manager AutomotiveLego Head of Customization ToysLimberry CEO Apparel & ShoesLumographics CEO Configurator SoftwareMAN Diesel Product Planning EnginesMaterialise Executive Vice President Manufacturing TechnologyMatteo Dosso CEO Apparel & ShoesMilk & Honey Co-Founder Apparel & ShoesMymsli CEO FoodMyswisschocolate CEO FoodMyVirtualModel Founder & CEO SoftwareNewton Industrial CEO IndustrialGroup InstrumentationNike Global Product Line Manager Apparel & ShoesNovolution CEO ConsultancyPfizer Director Strategy Established

Products EuropePharmaceutical

Porsche Head of Innovation Management AutomotivePossen CEO Apparel & ShoesProcter & Gamble Connect + Develop Leader FMCGSelve CEO Apparel & ShoesSpreadshirt European Marketing Manager Apparel & ShoesStrategic Horizons CEO ConsultancyTailorStore CEO Apparel & ShoesTechShop CEO Manufacturing TechnologyUnicatum CEO Apparel & ShoesVestas Innovation Manager Wind MillsVestel Design Architect Consumer ElectronicsWienand CEO Apparel & ShoesWittenstein Specialist Drive TechnologiesYouBar CEO FoodYouTailor Founder & CPO Apparel & ShoesZazzle Co-Founder & CPO MC IntermediaryZazzle Senior Director of Product De-

velopmentMC Intermediary

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Appendix 3: Initial item pool

Var External interaction competence (EIC)

v001 We employ a recommendation system to help our customers to identify theirspecific needs.

v002 We provide a configurator/ software tool to enable customers to find optimalproduct configurations for their specific needs.

v003 We involve our customers in the customization process by asking them toco-create their own solutions.

v004 We provide customers with realistic visualizations of their customized product.

v005 We are using augmented reality devices to help our customers to get a betterunderstanding of our product offering.

v006 During the customization process, we offer extensive information about allproduct configuration options.

v007 Customers can receive support from trained employees anytime throughoutthe entire product customization process.

v008 Our sales staff is well trained in order to help customers to concrete their needsand to customize their product.

v009 We try to reduce cognitive barriers of our customers by providing detailedinformation about the customization process.

v010 We try to educate our customers about our customization process.

v011 During the design of our customization process, we paid particular attentionto creating a joyful experience for our customers.

v012 During the design of our customization process, we paid particular attentionto creating a positive experience for our customers.

v013 During the design of our customization process, we paid particular attentionto creating a well-structured process for our customers.

v014 During the design of our customization process, we paid particular attentionto creating an easily understandable process for our customers.

v015 During the design of our customization process, we paid particular attentionto providing multiple customization channels.

v016 During the design of our customization process, we paid particular atten-tion to offer our customers an easy-to-use interface to provide all necessaryinformation.

v017 We help our customers to understand their own needs by providing detailedinformation about the product offering.

v018 We help our customers to get a better technical understanding of our productsby providing information about the interrelations between choice options.


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Var Internal interaction competence (IIC)

v019 We provide our customers with the option to track the status of their order.

v020 We provide our customers with CAD/CAM data that is automatically gener-ated from each customized product.

v021 We provide our customers with real-time feedback concerning delivery datesthroughout the entire customization process.

v022 We provide our customers with real-time feedback concerning product avail-ability anytime throughout the entire customization process.

v023 We provide our customers with real-time feedback concerning product pricesanytime throughout the entire customization process.

v024 We can quickly assess the cost structure of products that have been newlycustomized.

v025 After the customization process, our system automatically generates a productidentity code for the chosen product configuration.

v026 Customer orders are automatically translated into a bill of materials for eachcustomized product.

v027 We are using measurement devices to collect customer-specific data.

v028 We provide technical support systems to assist our sales staff.

Var Product design for process robustness (PDPR)

v029 We employ interdisciplinary teams to carry out the development of newproducts.

v030 We employ simultaneous/concurrent engineering to carry out the developmentof new products.

v031 We apply the principles of design for manufacturing/assembly (DFM/DFA)during the development of new products.

v032 We employ failure mode and effect analysis (FMEA) during the developmentof new products.

v033 We usually develop modular product architectures for new products.

v034 We usually develop integrated product architectures for new products.

v035 We develop our product architectures according to the principles of parametricdesign.

v036 During the development of new products we give particular attention to man-ufacturing requirements.

v037 During the development of new products we give particular attention to therequirements of production ramp-up.

v038 We apply the concept of product platforms in new product development.


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Var Process robustness (PR)

v039 We employ interdisciplinary teams to carry out the development of new man-ufacturing processes.

v040 We employ simultaneous/concurrent engineering to carry out the developmentof new manufacturing processes.

v041 We apply simulation techniques during the development of new manufacturingprocesses.

v042 We employ failure mode and effect analysis (FMEA) during the developmentof new manufacturing processes.

v043 Our manufacturing system relies mainly on pull-production.

v044 We apply postponement in manufacturing.

v045 We apply postponement in distribution logistics.

v046 Our supply chain logistics can quickly react to unexpected changes.

v047 Our supply chain logistics are characterized by prompt response times.

v048 We apply just-in-time (JIT) logistics for our manufacturing processes.

v049 We apply just-in-sequence (JIS) logistics for our manufacturing processes.

v050 Our manufacturing processes utilize computer-integrated manufacturing(CIM).

v051 Our manufacturing processes utilize flexible automation technology.

v052 Our manufacturing processes utilize robotics.

v053 Our manufacturing processes utilize rapid manufacturing technology.

v054 Our manufacturing processes can be reconfigured in a modular way.

v055 Our employees are trained so that they can be assigned flexibly within themanufacturing process.

v056 Our employees are trained to deal with novel and ambiguous tasks.

v057 Our manufacturing processes utilize a high degree of flow production.

v058 We apply quality function deployment to identify the customers’ key-value-attributes in our product domain.

v059 We apply conjoint analysis to identify the customers’ key-value-attributes inour product domain.

v060 We apply customer focus groups to identify the customers’ key-value-attributesin our product domain.

v061 We apply customer interviews to identify the customers’ key-value-attributesin our product domain.


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Var Initial solution space development (ISSD)

v062 We apply the lead user method to identify the customers’ key-value-attributesin our product domain.

v063 We apply customer surveys to identify the customers’ key-value-attributes inour product domain.

v064 We apply innovation toolkits to identify latent customer needs in our productdomain.

v065 We apply ethnography to identify latent customer needs in our productdomain.

v066 We apply netnography to identify latent customer needs in our productdomain.

v067 We apply idea contests to identify latent customer needs in our productdomain.

v068 We provide physical prototypes to evaluate our customers’ acceptance towardsproduct concepts.

v069 We employ rapid prototyping to quickly receive customer feedback on a largenumber of product concepts.

v070 We provide virtual prototypes to evaluate our customers’ acceptance towardsproduct concepts.

v071 We use auctioning mechanisms to evaluate our customers’ acceptance towardsproduct concepts.

v072 We bring product concepts into test markets to evaluate our customers’ ac-ceptance towards these concepts.

v073 We rely on principles of design of experience (DOE) when defining our productoffering.

v074 We apply benchmarking to learn from our competitors when defining ourproduct offering.

v075 We apply reverse engineering to get a better understanding of the technicalrequirements of our product domain.

v076 We apply the morphological box approach to get a better understanding ofthe technical scope of our product domain.

v077 We apply brainstorming to get a better understanding of the technical scopeof our product domain.

v078 We apply patent analysis to get a better understanding of the technical scopeof our product domain.

v079 We apply portfolio analysis to identify market potential for new productvariants.


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Var Adaptive solution space development (ASSD)

v080 We provide a feedback system for customers to make suggestions for newdesired product variants.

v081 We have regular meetings with key customers in order to monitor changes incustomer needs.

v082 We regularly contact our customers in order to monitor changes in customerneeds.

v083 We apply data mining in order to monitor changes in customer needs.

v084 We analyze discussions among relevant interest groups (e.g. online communi-ties) in order to monitor changes in customer needs.

v085 We apply trend analysis in order to monitor changes in customer needs.

v086 We have routines / processes for our sales staff to report changes in customerneeds that they have observed.

v087 We have routines / processes for our sales staff to report any pitfalls or short-comings of our offering that they have observed.

v088 We track our customers’ behavior within the customization process in orderto identify potential pitfalls or shortcomings of our offering.

v089 We analyze customer complaints in order to identify potential pitfalls or short-comings of our offering.

v090 We analyze past sales data in order to identify potential pitfalls or shortcom-ings of our offering.

v091 We analyze customer behavior in the configuration process in order to identifypotential pitfalls or shortcomings of our offering.

v092 We interview former customers in order to understand their reasons for switch-ing suppliers.

v093 We regularly contact our suppliers in order to be informed about technologicaladvances in our product domain.

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Appendix 4: Pre-test exercise & refined item pool

Assessment of the substantive validity according to Anderson and Gerbing (1991):Proportion of substantive agreement:psa= nc/N psa = Proportion of substantive agreement.

nc = Respondents assigning a measure to its posited construct.N = Total number of respondents.

Substantive-validity coefficient:csv= (nc-n0)/N csv = Substantive-validity coefficient.

nc = Respondents assigning a measure to its posited construct.n0 = Highest number of assignments of the item to any other construct.N = Total number of respondents.

Var External interaction competence (EIC)

v066 We provide a recommendation system to help our customers to identifytheir specific needs.

v067 We provide a configurator/ software tool to enable our customers to findthe optimal product configuration for their specific needs.

v069 We provide our customers with realistic visualizations of the customizedproduct during the customization process.

v070 We provide augmented reality devices to help our customers to get a betterunderstanding of our product offering.

v071 During the customization process, we offer extensive information about allproduct configuration options.

v072 Customers can receive support from trained employees anytime throughoutthe entire product customization process.

v073 We train our sales staff so that they can help customers to concrete theirneeds and to customize their product.

v074 We try to reduce cognitive barriers of our customers by providing detailedinformation about the customization process.

v075 We try to educate our customers about our customization process.

v076 During the design of our customization process, we paid particular attentionto creating a joyful / positive experience for our customers.

v078 During the design of our customization process, we paid particular attentionto creating a well-structured process for our customers.

v079 During the design of our customization process, we paid particular attentionto creating an easily understandable process for our customers.


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v081 During the design of our customization process, we paid particular attentionto offer our customers an easy-to-use interface.

v082 We help our customers to understand their own needs by providing detailedinformation about the product offering.

v083 We help our customers to get a better technical understanding of ourproducts by providing information about the interrelations between choiceoptions.

Var Internal interaction competence (IIC)

v084 We apply mechanisms/ routines that allow us to track the status of cus-tomer orders.

v085 We apply mechanisms/ routines that allow us to provide CAD/CAM datathat is automatically generated from each individual order.

v086 We apply mechanisms/ routines that allow us to provide real-time feedbackconcerning delivery dates throughout the entire customization process.

v087 We apply mechanisms/ routines that allow us to provide real-time feedbackconcerning product availability anytime throughout the entire customiza-tion process.

v088 We apply mechanisms/ routines that allow us to provide real-time feed-back concerning product prices anytime throughout the entire customiza-tion process.

v090 Our system automatically generates a product identity code for each or-dered product configuration.

v091 Customer orders are automatically translated into a bill of materials foreach customized product.

v092 We are using measurement technology that allows us to collect customer-specific data objectively.

v093 We provide technical support systems to assist our sales staff.

Var Product design for process robustness (PDPR)

v094 We apply interdisciplinary teams for the development of new products.

v095 We employ simultaneous/ concurrent engineering for the development ofnew products.

v096 We apply the principles of design for manufacturing/ assembly(DFM/DFA) during the development of new products.

v097 We employ failure mode and effect analysis (FMEA) during the develop-ment of new products.

v098 We develop modular product architectures for new products.

v099 We develop integrated product architectures for new products.


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v100 We develop our product architectures according to the principles of para-metric design.

v101 During the development of new products we give particular attention tomanufacturing requirements.

v102 During the development of new products we give particular attention tothe requirements of production ramp-up.

v103 During the development of new products we pursue a product platformstrategy.

v108 We apply interdisciplinary teams for the development of new manufacturingprocesses.

v109 We apply simultaneous/ concurrent engineering for the development of newmanufacturing processes.

v110 We apply simulation techniques during the development of new manufac-turing processes.

v111 We apply failure mode and effect analysis (FMEA) during the developmentof new manufacturing processes.

Var Process robustness (PR)

v112 We apply pull-production in our manufacturing system.

v113 We apply postponement in manufacturing.

v114 We apply postponement in distribution logistics.

v115 Our supply chain logistics can quickly react to unexpected changes.

v117 We apply just-in-time (JIT) logistics for our manufacturing processes.

v118 We apply just-in-sequence (JIS) logistics for our manufacturing processes.

v119 Our manufacturing processes utilize computer-integrated manufacturing(CIM).

v120 Our manufacturing processes utilize flexible automation technology.

v121 Our manufacturing processes utilize robotics.

v122 Our manufacturing processes utilize rapid manufacturing technology.

v123 Our manufacturing processes can be reconfigured in a modular way.

v124 Our employees are trained so that they can assigned flexibly within themanufacturing process.

Var Initial solution space development (ISSD)

v127 We apply quality function deployment to identify the customers’ key-value-attributes.

v128 We apply conjoint analysis to identify the customers’ key-value-attributes.


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v130 We apply customer interviews to identify the customers’ key-value-attributes.

v131 We apply the lead user method to identify the customers’ key-value-attributes.

v132 We apply customer surveys to identify the customers’ key-value-attributes.

v137 We apply physical prototypes to evaluate our customers’ acceptance to-wards product concepts.

v138 We apply rapid prototyping to evaluate our customers’ acceptance towardsproduct concepts.

v139 We apply virtual prototypes to evaluate our customers’ acceptance towardsproduct concepts.

v141 We apply test markets to evaluate our customers’ acceptance towards theseconcepts.

v143 We apply benchmarking to learn from our competitors when defining ourproduct offering.

v146 We apply systematical monitoring of technological advances in our productdomain before defining our product offering.

v148 We apply portfolio analysis to identify market potential for new products.

Var Adaptive solution space development (ASSD)

v149 We provide a feedback system for customers to make suggestions for newdesired product variants.

v151 We regularly contact our customers in order to monitor changes in customerneeds.

v152 We apply data mining in order to monitor changes in customer needs.

v154 We apply trend analysis in order to monitor changes in customer needs.

v155 We have routines/ processes for our sales staff to report changes in customerneeds that they have observed.

v158 We analyze customer complaints in order to identify potential pitfalls orshortcomings of our offering.

v159 We analyze sales and revenue data of all product variants in order to identifypoorly performing variants.

v160 We analyze customer behavior in the configuration process in order to iden-tify potential pitfalls or shortcomings of our offering.

v161 We interview former customers in order to understand their reasons forswitching to competitors.

v163 We apply portfolio analysis to identify market potential for additional prod-uct variants.

161

Appendix 5: Questionnaire items

External interaction competence (EIC) (newly developed)

Please indicate to which extent you agree with the following statementsabout your organization (7 point Likert scale with the anchors ”stronglydisagree” and ”strongly agree”):

EICr1∗ Considering the high level of product variety that we offer our customers,we are doing a good job in reducing choice complexity.

EICr2∗ We have found ways to purposefully support our customers in searching,identifying, and specifying suitable product variants.

EICr3∗ Our sales process offers customers a high degree of usability in terms ofproduct specification.

Please indicate to which extent your organization engages in the followingactivities (7 point Likert scale with the anchors ”not at all” and ”veryextensively”):

EICf1 We provide a recommendation system to help our customers to identifytheir specific needs.

EICf2 We provide a configurator/ software tool to enable our customers to findthe optimal product configuration for their specific needs.

EICf3 We provide our customers with realistic visualizations of the customizedproduct during the customization process.

EICf4 We provide augmented reality devices to help our customers to get a betterunderstanding of our product offering.

EICf5 During the customization process, we offer extensive information about allproduct configuration options.

EICf6 Customers can receive support from trained employees anytime throughoutthe entire product customization process.

EICf7 We train our sales staff so that they can help customers to specify theirneeds and to customize their product.

EICf8 We try to reduce cognitive barriers of our customers by providing detailedinformation about the customization process.

EICf9 We try to educate our customers about our customization process.

EICf10 During the design of our customization process, we paid particular attentionto creating a joyful/ positive experience for our customers.

EICf11 During the design of our customization process, we paid particular attentionto creating a well-structured process for our customers.

EICf12 During the design of our customization process, we paid particular attentionto creating an easily understandable process for our customers.


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EICf13 During the design of our customization process, we paid particular attentionto offer our customers an easy-to-use interface.

EICf14 We help our customers to understand their own needs by providing detailedinformation about the product offering.

EICf15 We help our customers to get a better technical understanding of ourproducts by providing information about the interrelations between choiceoptions.

Internal interaction competence (IIC) (newly developed)


IICr1∗ We have found ways to efficiently deal with the increased inbound andoutbound information flow of customer specific data that results from thehigh level of product variety that we offer.

IICr2∗ Our internal IT systems can effectively handle all relevant data forcustomer-specific orders.

IICr3∗ With the help of certain tools / methods / technology we can provide ourcustomers with real-time information (pricing, delivery dates, etc.) duringthe customization process.


IICf1 We apply mechanisms/ routines that allow us to track the status of cus-tomer orders.

IICf2 We apply mechanisms/ routines that allow us to provide CAD/CAM datathat is automatically generated from each individual order.

IICf3 We apply mechanisms/ routines that allow us to provide real-time feedbackconcerning delivery dates throughout the entire customization process.

IICf4 We apply mechanisms/ routines that allow us to provide real-time feedbackconcerning product availability anytime throughout the entire customiza-tion process.

IICf5 We apply mechanisms/ routines that allow us to provide real-time feed-back concerning product prices anytime throughout the entire customiza-tion process.

IICf6 Our system automatically generates a product identity code for each or-dered product configuration.

IICf7 Customer orders are automatically translated into a bill of materials foreach customized product.


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IICf8 We are using measurement technology that allows us to collect customer-specific data objectively.

IICf9 We provide technical support systems to assist our sales staff.

Product design for process robustness (PDPR) (newly developed)


PDPRr1∗ The product architecture has been carefully designed and implemented inorder to be able to provide all available product variants with near massproduction efficiency and at a constant level of quality.

PDPRr2∗ We have found ways to develop product architectures that allow a highdegree of operational flexibility and efficiency in manufacturing.

PDPRr3∗ The design of our product architecture facilitates the efficient manufactur-ing of products in small batch sizes.

PDPRr4∗ Our product architecture enables us to efficiently manufacture our completerange of product variants, as if they all were the same.

Please indicate to which extent your organization engages in the follow-ing activities (7 point Likert scale with the anchors ”not at all’ and ”veryextensively”):

PDPRf1 We apply interdisciplinary teams for the development of new products.

PDPRf2 We apply simultaneous/ concurrent engineering for the development of newproducts.

PDPRf3 We apply the principles of design for manufacturing/ assembly (DFM/DFA) during the development of new products.

PDPRf4 We apply failure mode and effect analysis (FMEA) during the developmentof new products.

PDPRf5 We develop modular product architectures for new products.

PDPRf6 We develop integrated product architectures for new products.

PDPRf7 We develop our product architectures according to the principles of para-metric design.

PDPRf8 During the development of new products we give particular attention tomanufacturing requirements.

PDPRf9 During the development of new products we give particular attention tothe requirements of production ramp-up.

PDPRf10 During the development of new products we pursue a product platformstrategy.


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Process robustness (PR) (newly developed)


PRr1∗ The manufacturing processes have been carefully designed and implementedin order to be able to provide all available product variants with near massproduction efficiency and at a constant level of quality.

PRr2∗ We have found ways to efficiently cope with the additional production com-plexity that results from the level of product variety that we offer ourcustomers.

PRr3∗ Our manufacturing processes are designed to efficiently handle small batchsizes and frequent changes of the production set-up.

PRr4∗ Due to the design of our manufacturing processes, frequent changes of theproduction set-up do not induce significantly higher costs.


PRf1 We apply interdisciplinary teams for the development of new manufacturingprocesses.

PRf2 We apply simultaneous/ concurrent engineering for the development of newmanufacturing processes.

PRf3 We apply simulation techniques during the development of new manufac-turing processes.

PRf4 We apply failure mode and effect analysis (FMEA) during the developmentof new manufacturing processes.

PRf5 We apply pull-production in our manufacturing system.

PRf6 We apply postponement in manufacturing.

PRf7 We apply postponement in distribution logistics.

PRf8 Our supply chain logistics can quickly react to unexpected changes.

PRf9 We apply just-in-time (JIT) logistics for our manufacturing processes.

PRf10 We apply just-in-sequence (JIS) logistics for our manufacturing processes.

PRf11 Our manufacturing processes utilize computer-integrated manufacturing(CIM).

PRf12 Our manufacturing processes utilize flexible automation technology.

PRf13 Our manufacturing processes utilize robotics.


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PRf14 Our manufacturing processes utilize rapid manufacturing technology.

PRf15 Our manufacturing processes can be reconfigured in a modular way.

PRf16 Our employees are trained so that they can be assigned flexibly within themanufacturing process.

Initial solution space development (ISSD) (newly developed)


ISSDr1∗ Within the technical, legal, and economic boundaries of our product do-main, we are able to define an initial set of customizable product attributesthat meets the heterogeneous requirements of a broad group of customers,so that almost every individual customer can find a suitable product.

ISSDr2∗ We have the capability to develop a thorough understanding of the technicalrequirements and the customer value-drivers before market launch, whichallows us to clearly define an initial set of customizable product attributesthat grasps the heterogeneity of customer needs in our market.

ISSDr3∗ We have found ways to efficiently develop an initial set of customizableproduct attributes according to the specific situation of our internal andexternal business environment that addresses a large numbers of differentcustomer requirements.


ISSDf1 We apply quality function deployment to identify the customers’ key-value-attributes.

ISSDf2 We apply conjoint analysis to identify the customers’ key-value-attributes.

ISSDf3 We apply customer interviews to identify the customers’ key-value-attributes.

ISSDf4 We apply the lead user method to identify the customers’ key-value-attributes.

ISSDf5 We apply customer surveys to identify the customers’ key-value-attributes.

ISSDf6 We apply physical prototypes to evaluate our customers’ acceptance to-wards product concepts.

ISSDf7 We apply rapid prototyping to evaluate our customers’ acceptance towardsproduct concepts.

ISSDf8 We apply virtual prototypes to evaluate our customers’ acceptance towardsproduct concepts.


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ISSDf9 We apply test markets to evaluate our customers’ acceptance towards newproduct concepts.

ISSDf10 We apply benchmarking to learn from our competitors when defining ourproduct offering.

ISSDf11 We apply systematical monitoring of technological advances in our productdomain before defining our product offering.

ISSDf12 We apply portfolio analysis to identify market potential for new products.


Adaptive solution space development (ASSD) (newly developed)


ASSDr1∗ We have defined processes to evaluate the quality of fit of the existing rangeof product variants and to adapt our offering if necessary.

ASSDr2∗ We regularly check the fit of our existing set of product variants with ourcustomers’ expectations and revise, trim or extend the available productassortment accordingly.

ASSDr3∗ We have found ways to efficiently refine our product range on a regular basisin order to comply with changing customer needs and new technologies.

ASSDr4∗ We regularly re-examine our understanding of the technical requirementsand the customer value-drivers of our market, so that we are at all timesable to provide a set of product variants that meets the heterogeneousrequirements of a broad group of customers in a specific product domain.


ASSDf1 We provide a feedback system for customers to make suggestions for newdesired product variants.

ASSDf2 We regularly contact our customers in order to monitor changes in customerneeds.

ASSDf3 We apply data mining in order to monitor changes in customer needs.

ASSDf4 We apply trend analysis in order to monitor changes in customer needs.

ASSDf5 We have routines/ processes for our sales staff to report changes in customerneeds that they have observed.

ASSDf6 We analyze customer complaints in order to identify potential pitfalls orshortcomings of our offering.


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ASSDf7 We analyze sales and revenue data of all product variants in order to identifypoorly performing variants.

ASSDf8 We analyze the customer behavior in the configuration process in order toidentify potential pitfalls or shortcomings of our offering.

ASSDf9 We interview former customers in order to understand their reasons forswitching to competitors.

ASSDf10 We apply portfolio analysis to identify market potential for additional prod-uct variants.

Market performance (MP) (Irving, 1995)

Relative to your competitors, how has your company performed over thelast three business years with respect to:

MP1 Providing customer benefit

MP2 Attaining desired market share

MP3 Achieving customer satisfaction

Degree of customization (DC) (Lampel & Mintzberg, 1996)

Please select the strategic approach from the following list, which corre-sponds most with the business model of your company:

DC1 Pure standardization (mass production): We target the broadest pos-sible group of customers, produce on as large a scale as possible, and dis-tribute a single design commonly to all. Customers have no direct influenceover design, production, or even distribution decisions.

DC2 Segmented standardization (build to forecast): We respond to theneeds of different clusters of customers, but each cluster remains aggre-gated. The offered products are standardized within a narrow range offeatures. A basic product design is modified and multiplied to cover vari-ous product dimensions but not at the request of individual customers.

DC3 Customized standardization (assemble to order): Each customergets his/ her own configuration but constrained by the range of availableoptions. The basic product design is not customized, and the componentsare all mass produced for the aggregate market. Thus, products are madeto order from standardized components, but assembly is customized foreach individual customer.

DC4 Tailored customization (made to order): We present a product pro-totype to a potential customer and then adapt or tailor it to the individualneeds. Customization works backward to the fabrication stage but not tothe design stage.


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DC5 Pure customization (engineer to order): Customers’ needs influencedecision making at all stages design, fabrication, assembly, and distribu-tion. The product is truly engineered to order and all stages are largelycustomized.

Notes. ∗Reflectively measured items.

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Paper 3

High-variety product offerings

and company performance: The

mediating roles of robust process

design and interaction

competence

Abstract: Prior studies have emphasized that various organizational capabilities are

crucial for companies pursuing a high-variety product strategy. Necessary is their solu-

tion space development capability, which describes a company’s ability to develop a set of

product variants that allows addressing a broad range of customers with heterogeneous

needs as accurately as possible. However, literature suggests that further capabilities

are required for successfully pursuing high-variety strategies. First, a robust process

capability is required in manufacturing, targeting a flexible but efficient manufacturing

system. Second, an interaction competence capability is necessary in marketing, aim-

ing at the efficient exchange of information with customers during the customization

process. Despite the high relevance of these capabilities for the success of companies

pursuing a high-variety strategy, our understanding of their interrelations is limited. In

this study, a conceptual model is derived from theory and empirically tested using a sam-

ple of 193 manufacturing companies. Results highlight that interaction competence and

187

188

robust process design mediate the direct effect of solution space development on com-

pany performance. Findings also indicate that the three capabilities influence financial

success indirectly through company performance. These results can help scholars and

managers to better understand the foundation of sustainable company performance and

financial success of companies pursuing a high-variety product strategy. Furthermore, it

can help managers conducting strategic considerations and deriving priorities to enhance

company performance.

Keywords: High-variety product strategy, mass customization, capabilities,

organizational configuration, company performance.

Status:

• Working paper.

• Paper will be submitted to a peer-reviewed journal for publication.

Presented at:

• TIME doctoral seminar, May 2014, RWTH Aachen University.

• Accepted for presentation at the Academy of Management Annual Meeting 2015,

Vancouver, Canada.

189

Introduction

As a prerequisite for success in the long term, companies need to adapt themselves to

satisfying customer needs better than their competitors (Saxe & Weitz, 1982; Homburg

et al., 2009). Therefore, an accurate identification of customer needs in a target market

and their translation into adequate product offerings is crucial (Kohli & Jaworski, 1990).

As companies are confronted with continuously increasing demand for new product

functions and features, regionally differing product requirements, and customers with

heterogeneous needs (van Dolen et al., 2002; Franke et al., 2009; ElMaraghy et al., 2013),

they face the problem of adequately aligning their product offering to such diverse and

dynamic business environments. In consequence, many companies start pursuing a mass

customization (MC) strategy, thereby increasing the number of offered product variants

dramatically (Piller, 2004; ElMaraghy et al., 2013).

However, establishing and pursuing MC successfully requires companies to develop

specific capabilities (Pine et al., 1993; Duray, 2002; Piller, 2004; Salvador et al., 2009;

Fogliatto et al., 2012). Based on the resource-based view, Salvador et al. (2009)

suggest a strategic capability framework for companies with high-variety product offer-

ings comprising three capability dimensions that can be assigned to the functional areas

of product development, manufacturing, and marketing. This framework has later been

extended and refined by Wellige and Steiner (2014). The solution space develop-

ment (SSD) capability refers to the identification of key value attributes of products

along which customer needs diverge most and the definition and maintenance of an ap-

propriate product offering. The second capability, referred to as robust process design

(RPD), addresses the need to possess highly flexible but efficient manufacturing systems,

which enable companies to produce smallest production batches at a similar efficiency

level as mass production systems. Finally, it is suggested that companies pursuing a

MC strategy need to develop the capability of interaction competence (IC) aiming to

achieve efficient and effective information exchange with customers and the processing

of all data associated with customization processes. Beside this framework, which pro-

vides a holistic and strategic perspective on MC, there is a lot of literature focused on

single capabilities or aspects of MC, such as aspects of product configuration processes

or manufacturing systems (an overview provides e.g. Fogliatto et al. (2012)).

190

However, although the development of specific capabilities for MC in different func-

tional areas of a company has been regarded as necessary for the competitiveness and

success of these companies, the relationship between these specific capabilities and their

relations to company performance has received little attention in empirical research. To

address these research gaps, a conceptual framework, based on Wellige and Steiner

(2014), is developed in this study that incorporates the strategic capabilities for MC

and organizational, market, and financial performance outcomes. Thereby, following

literature on MC and related fields, it is expected that company performance is an out-

come of a company’s ability to develop a solution space or product offering that fits

different customer needs as good as possible. We suggest that the two other capabilities,

which relate to the downstream processes of manufacturing and marketing, mediate the

relationship between SSD and company performance.

By addressing these research issues, this study adds to the dialog on MC in at least

two important ways. First, the relationship between the three capabilities and their

common role as predictors of company performance is investigated relying on the most

comprehensive capabilities framework available, which has not been considered in previ-

ous research. We provide new insights on the relationships between the three capabilities

and their impact on company performance, which helps to improve our understanding

about MC and its drivers of success. Second, the performance implications of these three

capabilities are examined in an industry context for the first time by using a sample of

business-to-business (B2B) companies. We believe that B2B markets and the domain of

industrial goods is very suitable for the object of this study. Customers in this domain

expect a very high fit between their product requirements and the delivered product

(Zhang & Tseng, 2009; Ellis, 2010). This frequently forces companies in B2B markets

to offer high variety and customizable products in order to avoid that customers do not

buy at all (Ellis, 2010). Therefore, as high-variety product offerings in this domain are

commonly and widespread, we expect that a B2B sample is most appropriate for our

research purpose.

In the following section, we present a literature review and develop hypotheses that

underly our empirical model. Subsequently, we discuss the design of the study, the

measures, and the descriptive statistics of the study. The next section discusses the

evaluation of the measures and the empirical procedures, followed by a presentation of

191

the findings and the test of the hypotheses. Finally, we discuss the results, implications

for academics and companies, limitations, and directions for future research.

Theory and hypotheses

Strategic capabilities for mass customization

Literature suggests that companies pursuing a MC strategy need to develop and imple-

ment certain capabilities as requisite for company performance and success (e.g. Salvador

et al., 2009; Fogliatto et al., 2012; Harzer, 2013; Wellige & Steiner, 2014). This is in

line with the resource-based view and the capabilities-based view of the firm, which sug-

gest that performance differences between companies are a result of unevenly allocated

resources and differences in companies’ abilities to deploy their resources (Wernerfelt,

1984; Barney, 1991; Grant, 1991; Amit & Schoemaker, 1993; Teece et al., 1997; Makadok,

2001). According to the capabilities framework for MC, three distinct capabilities in the

functional areas of product development, manufacturing and marketing are required

(Salvador et al., 2009). Thereby, capabilities are understood as a company’s ability

to orchestrate and make use out of distinct bundles of managerial activities and orga-

nizational resources related to the SSD, RPD, and IC capabilities (Wellige & Steiner,

2014).

The solution space development capability enables companies to develop a product

offering that preferably provides each single customer a product variant with a higher

preference fit compared to a standard product. Therefore, on the one hand, companies

require distinct managerial activities and resources to generate market intelligence about

customer requirements and needs in the product domain in order to identify those prod-

uct attributes that drive customers’ perceived value and their willingness to pay (see

MacCarthy et al., 2002; Zhang & Tseng, 2007; Salvador et al., 2009). On the other

hand, companies are also forced to take technological and economic aspects into account

when setting up their product offering, as unlimited choice is not feasible without losing

the required operational efficiency (Pil & Holweg, 2004; Piller, 2004; Wan et al., 2012;

ElMaraghy et al., 2013). Wellige and Steiner (2014) further detailed the definition.

Accordingly, SSD refers to all specific product development and product management

activities necessary to define a high-variety product offering for such markets before

192

launch. Furthermore, it is associated with activities related to the constant evaluation

of the fit between the product offering and customer needs in the target market, and,

if necessary, to revise the offered range of product variants. In consequence, high levels

of a SSD capability are associated with a high-variety product offering that is in line

with the economic abilities of a company, market requirements, and thereby as lean as

possible.

The robust process design capability addresses the issue of bridging high levels of

efficiency and flexibility in manufacturing systems. Manufacturing units of companies

offering high variety oftentimes face increased levels of complexity, uncertainty, and re-

sponsiveness induced by the need to fulfill customer specific orders without long delivery

lead times (Su et al., 2005). This usually is associated with high costs in manufacturing

and related operations (Piller, 2004; Su et al., 2005). To be able to operate a flexi-

ble but efficient manufacturing system, companies pursuing a MC require a bundle of

managerial activities and organizational resources, such as flexible automation technolo-

gies, that together add up to a company’s RPD capability. Companies that possess

a strong RPD capability are more likely able to manufacture and deliver customized

products on efficiency and reliability levels quite close to a comparable mass production

system (Salvador et al., 2009). Beside the aspects related to manufacturing systems

for high-variety product offerings, RPD also encompasses a company’s ability to deter-

mine a product architecture that facilitates high levels of product variety, which helps

to maintain efficiency in manufacturing processes (Wellige & Steiner, 2014).

The interaction competence capability is targeted on the reduction of costs associ-

ated with the interaction and information exchange between a company pursuing a MC

and its customers as well as those costs related to the processing of all customer-related

information (Wellige & Steiner, 2014). Offering customizable products to customers re-

quires providing them comprehensive information about the product and its modifiable

attributes (Claycomb et al., 2005; Franke et al., 2009). Thereby, it is crucial not to over-

strain them by the potentially unmanageable number of product variants (Huffman &

Kahn, 1998; Dellaert & Dabholkar, 2009; Anderson & Swaminathan, 2011). The IC ca-

pability encompasses specific managerial activities and organizational resources. These

enable a company to keep the costs associated with these interaction and information

exchange processes as low as possible for both, companies and customers, while increas-

ing the overall value for the customer at the same time (Merle et al., 2010; Wellige &

193

Steiner, 2014). Furthermore, the IC capability refers to the ability of a company to

efficiently manage the complex internal information processing and provision (Wellige

& Steiner, 2014). Overall, higher levels of the IC capability are associated with more

efficient interaction processes, information exchange, and information processing.

Despite the widespread agreement in literature that companies pursuing a MC strat-

egy need these specific capabilities, there is surprisingly little empirical evidence on the

interrelation of these capabilities (Wellige & Steiner, 2014). This study addresses this

gap by taking a configurational perspective on such companies. The configurational

approach is part of the contingency theory (e.g. Drazin & Van de Ven, 1985; Venka-

traman, 1989; Donaldson, 2001) and takes a holistic view on the company context and

design (van de Ven et al., 2013). This perspective defines that fit between contextual

factors, strategy, and organizational structure enables companies to gain a competitive

advantage over other companies lacking this fit (Miller & Friesen, 1984; Miller, 1986).

Companies are considered as holistic entities, comprising of a set of subcomponents that

relate to each other and form organizational configurations (van de Ven et al., 2013).

Theorists suggest that for each strategic orientation, there exists an ideal configuration

that enables companies to achieve high levels of performance and their strategic goals

(Ketchen et al., 1993; Vorhies & Morgan, 2003). However, it is also recognized that a

perfect fit is nearly unattainable, prevented, for example, by multiple and often conflict-

ing goals within companies (van de Ven et al., 2013). Rather, it is expected for a distinct

strategic setting that a small number of organizational configurations emerge over time

with aligned key variables that converge to the ideal profile (Wiklund & Shepherd,

2005). In this reasoning, high levels of performance are achievable when a company’s

configuration is characterized by high levels of internal consistency and a good fit with

contextual factors (Ketchen et al., 1993; Miller, 1996). Therefore, researchers should at-

tempt to identify feasible sets of internally consistent patterns or configurations relevant

for company performance (Miller, 1981; Venkatraman, 1989).

In the case of MC, recent literature is primarily focused on the synergetic benefits of

possessing all three capabilities (Harzer, 2013). A logical sequence of the three capabil-

ities, e.g. an internal consistent configuration of the MC capabilities within companies,

has not been researched until now. However, especially the connections and sequences

between the capabilities on strategic and on operational level, for example between the

capabilities for SSD and RPD, are crucial. Many examples of costly failures during the

194

implementation of MC strategies are documented, oftentimes as a result of misaligned

or missing capabilities (Gownder et al., 2011; Gandhi et al., 2014), suggest that it might

be valuable for practitioners and researchers to better understand the configuration of

MC capabilities within a company. Thus, following the literature on the configurational

approach, we conceptualize a configuration for companies pursuing MC strategies. In

the literature on marketing, new product development, and MC, a high fit between cus-

tomer needs and a company’s product offering is a main driver of company performance

(Kohli & Jaworski, 1990; Kirca et al., 2005; Yannopoulos et al., 2012). Thus, SSD, as

an essential capability of product development located at an early stage of the value

chain, can be seen as a valid starting point when creating a conceptual configuration

for companies pursuing MC strategies. However, as argued above, a SSD capability by

itself is insufficient for companies to reach high levels of performance when pursuing the

strategic goal of making profit of heterogeneous customer needs. Companies also need

to invest downstream within the value chain in the development and implementation

of activities and resources related to the RPD capability in manufacturing as well as

the IC capability in marketing. Furthermore, all three capabilities have to be aligned

with each other in order to be able to produce and market the high variety available in

the solution space successfully. Therefore, we suggest that the two capabilities of RPD

and IC mediate the link between a company’s ability to align its product offering with

the market requirements and company performance (see Figure 3.1). In the following

sections, the interrelations between the three capabilities and their relations to company

performance are hypothesized in detail.

Figure 3.1: Hypothesized model

195

Solution space development and company performance

Research on marketing and new product development reveals that company performance

strongly depends on knowing the needs and wants of customers in the target market as

well as the development and delivery of products that meet those needs (Kohli & Ja-

worski, 1990; Kirca et al., 2005; Yannopoulos et al., 2012). However, in today’s business

environments, it is increasingly difficult to develop a product offering that suits market

requirements, due to fast changing and diverging customer needs (van Dolen et al., 2002;

Franke et al., 2009; ElMaraghy et al., 2013). As mentioned above, the SSD capability

fundamentally enables companies to develop a high-variety product offering that allows

to address a broad range of customers diverse needs, and thus, to deal with such market

conditions better than standardized product offerings developed for large market seg-

ments of average customers based on classical market research methods (Salvador et al.,

2009; Wellige & Steiner, 2014).

The increased preference fit of customized products compared to of-the-shelf prod-

ucts creates added value for the customers (Franke & Piller, 2004; Franke et al., 2009).

Furthermore, studies reveal that a higher preference fit provided by customized products

also increases customer satisfaction (Tu et al., 2001), customer loyalty (Wind & Ran-

gaswamy, 2001), and customers’ willingness to pay a price premium (Franke & Piller,

2004). In another study, a positive link between the SSD capability and company per-

formance through a solution space quality construct is observed (Steiner, 2014). In turn,

these results suggest a positive impact of the SSD capability on company performance.

As SSD shares different aspects with the market orientation construct (e.g. Kohli

& Jaworski, 1990; Narver & Slater, 1990; Jaworski & Kohli, 1993), empirical evidence

on the market orientation construct might provide some insights to further improve

the understanding about the relationship between the SSD capability and company

performance. Kohli and Jaworski (1990) conceptualize market orientation from a

behavioral perspective as organizational activities related to the generation and dis-

semination of market intelligence that comprises customers’ current and future needs.

Market orientation is differentiated into proactive market orientation, which refers to the

identification and satisfaction of latent and unarticulated needs, and responsive market

orientation, which focuses on acquisition and use of information about explicit customer

needs (Slater & Narver, 1998; Jaworski et al., 2000; Narver et al., 2004; Atuahene-Gima

196

et al., 2005; Yannopoulos et al., 2012). Similar, the SSD capability includes a strong cus-

tomer orientation by comprising activities related to the identification of latent as well

as explicit needs and their translation into a product offering (Wellige & Steiner, 2014).

In line with market orientation, SSD encompasses activities related to the monitoring of

the business environment such as tracking of activities of competitors or technological

developments. Studies related to the market orientation construct demonstrate a posi-

tive effect on organizational performance (see Jaworski & Kohli, 1993; Slater & Narver,

1994; Atuahene-Gima, 1995; Pelham & Wilson, 1996; Baker & Sinkula, 1999; Kirca

et al., 2005). Other studies propose that market orientation helps to enhance customer-

perceived quality of products and services (Jaworski & Kohli, 1993; Pelham & Wilson,

1996; Brady & Cronin, 2001), customer satisfaction and loyalty (Slater & Narver, 1994),

product advantage (Langerak et al., 2004), and innovativeness and new product per-

formance in terms of market share, sales, return on investment, and profitability (see

Narver & Slater, 1990; Jaworski & Kohli, 1993; Day, 1994; Slater & Narver, 1994; Baker

& Sinkula, 1999; Im & Workman Jr, 2004; Kirca et al., 2005). Overall, we hypothesize

the following relationship:

Hypothesis 1: The solution space development capability has a positive impact on

company performance.

However, we expect that this effect is only indirect through the RPD and IC capa-

bilities, which will be discussed in detail in the following.

The mediating role of the robust process design capability

As argued above, offering customized products does not only create the opportunity of

increased revenues by addressing each customer’s needs more precisely compared to a

standard product offering, but may also reduce operational efficiency (Tu et al., 2001;

Squire et al., 2006; Huang et al., 2008; ElMaraghy et al., 2013). In this context, high

levels of product variety, small production batches and the complexity and uncertainty

in manufacturing that is associated with increased product variety are drivers of manu-

facturing costs (Su et al., 2005; Wan et al., 2012; ElMaraghy et al., 2012). This forces

companies pursuing a MC strategy to limit the customizable product attributes to those

that drive customers’ perceived value and their willingness to pay during the development

of a solution space by comparing the marginal costs of each additional customization

197

option to its marginal profit. Thus, higher levels of the SSD capability lead to more

focused high-variety product offerings, and thus, can help to prevent unnecessary ex-

penses in manufacturing, assembly, and logistics as these functional areas are confronted

with lower levels of complexity and uncertainty. Tu et al. (2004) show that activities

related to the SSD capability, such as practices related to monitor changes in customer

needs, positively impact a company’s ability to produce high variety efficiently.

Furthermore, market intelligence about the product attributes in which customer

needs differ most may also help companies to more precisely cluster the product in those

attributes that are common across all product variants and those requiring variety. This

should facilitate a company’s choice about the product architecture that reflects the

balance between commonality and variety best, which in turn contributes to a reduction

of complexity and costs in manufacturing and associated processes.

Key enablers mitigating the trade-off between high variety and highly efficient man-

ufacturing are flexible manufacturing technologies, such as robotics, computer integrated

manufacturing, or rapid manufacturing technologies, as well as modular product archi-

tectures (Fogliatto et al., 2012). Product architectures relying on modularity, such as

product platforms or modular products, enable companies to reduce costs of producing

high variety (ElMaraghy et al., 2013). For example, product platforms, defined as a col-

lection of components, processes, knowledge, people and their relations shared among a

set of products, allow addressing different customer requirements by deriving customized

products from this common platform while maintaining economy of scale (Robertson &

Ulrich, 1998; Jiao et al., 2007; Thomas et al., 2014). Furthermore, product platforms as

a mean of a high-variety strategy reduce engineering costs in the long run and enable

companies to increase market share (ElMaraghy et al., 2013).

Corresponding to product platforms, process platforms are characterized by a joint

process structure enabling companies to derive process variants to produce each product

variant while maintaining high levels of manufacturing efficiency (Jiao et al., 2007). Tu

et al. (2001) show that companies with higher levels of flexibility, robustness, and

efficiency in their manufacturing systems, achieve higher levels of customers satisfaction

with regard to product quality and provided product features, more loyal customers,

and higher recommendation rates. A simulation study provides indication that higher

levels of flexibility in manufacturing systems for high variety leads to reductions in

198

lead time and inventory holding, thereby helping to increase customer satisfaction and

cost efficiency (Brabazon et al., 2010). Beside technologies, companies should invest in

human resource flexibility, in terms of employee attributes such as skills, behaviors, and

practices, as they are positively related to company performance (Bhattacharya et al.,

2005).

Overall, companies achieving high levels of a RPD capability should be able to

better harmonize high-variety product offerings with company performance.

Hypothesis 2: The robust process design capability mediates the relationship be-

tween the solution space development capability and company performance.

The mediating role of the interaction competence capability

Interacting with customers in order to exchange information is a necessary process when

customizing products according to customers’ needs (Claycomb et al., 2005; Franke et al.,

2009). A company’s SSD capability may help to cope with this task as it encompasses

the ability to gain a thorough understanding of customer needs by identifying those

product attributes in which needs differ most and that drive customers’ perceived value

(Wellige & Steiner, 2014). This might also help companies to focus the interaction

and customization processes on the most relevant aspects, and thus, to guide customers

more purposefully through these processes. Similar to RPD, a more focused and aligned

high-variety product offering, as a result of a high SSD capability, should help to reduce

the complexity of the interaction and configuration processes. In consequence, the SSD

capability can help to increase effectiveness and efficiency of interactions and information

exchange between customers and companies.

Helping customers to understand and express their own preferences increases their

attitude toward the customized product compared to a standard product (Franke et al.,

2009). Furthermore, it increases customers’ intention to buy a customized product

and their willingness to pay (Franke et al., 2009). For customization processes there is

evidence that customer satisfaction is driven by the accessibility of relevant information

in real time and the perceived ease of the transaction process (Huffman & Kahn, 1998;

Dellaert & Dabholkar, 2009; Chang & Chen, 2009). To be able to efficiently provide

real time information about customized products, such as feasibility and delivery dates

199

(Ninan & Siddique, 2006), technical details and visualizations (Chang & Chen, 2009;

Trentin et al., 2011), or prices (Dellaert & Dabholkar, 2009; Franke et al., 2009; Trentin

et al., 2011), companies rely on information systems, product configurators (Dietrich

et al., 2007; Randall et al., 2007; Fogliatto et al., 2012), and sales support systems

for their sales representatives (Franke & Piller, 2003; Fiore et al., 2004; Piller, 2004;

Salvador et al., 2004; Randall et al., 2007). Implementing product configurators enables

companies to carry out interaction and information transfer with customers efficiently

and effectively (Hvam et al., 2008; Trentin et al., 2012). Furthermore, configuration

systems help customers to identify their needs, and thus, to achieve a better fit between

the idiosyncratic needs of customers and the product delivered by the company (Hvam

et al., 2006; Forza & Salvador, 2008; Trentin et al., 2012). Furthermore, by providing

technical devices to support customers in exploring their own needs companies can

help to reduce their perceived complexity of configuration processes (Ninan & Siddique,

2006), and in turn, increase process satisfaction. Studies reveal that tailoring services,

information and the shopping experience to each single customer has a positive impact

on customer satisfaction (Anderson & Swaminathan, 2011; Thirumalai & Sinha, 2011).

Moreover, the opportunity of a direct interaction with sales representatives during the

configuration process enhances customers’ perception of the achievable value of a product

and increases their enjoyment, shopping experience, and perceived control about the

customization process and its outcome (Dellaert & Dabholkar, 2009).

Besides supporting the interaction and information exchange during the configura-

tion process, configuration systems can support the order fulfillment and manufacturing

processes (Salvador et al., 2004; Trentin et al., 2012), for example through the au-

tomatic generation of bills of material (Aydin & Gungor, 2005; Dietrich et al., 2007;

Trentin et al., 2011). Additionally, configuration systems help to reduce errors during

the product configuration process (Hvam et al., 2004) in terms of the correct description

of product variants (Forza & Salvador, 2002), or during the generation of manufacturing

data and bills of material (Salvador & Forza, 2004; Heiskala et al., 2007). Reducing errors

during configuration and ordering processes helps to improve customers’ perceived prod-

uct quality (Trentin et al., 2012) and to increase operational performance of companies

(Trentin et al., 2011).

Overall, companies possessing high levels of a IC capability should be able to better

harmonize the large amount of product variants offered to customers and company

200

performance.

Hypothesis 3: The interaction competence capability mediates the relationship be-

tween the solution space development capability and company performance.

Company performance and financial performance

Literature provides evidence to suggest that organizational performance and market

performance, as proxies of company performance, are required antecedence of financial

success of a company. Homburg and Pflesser (2000) show that a company’s market

performance is positively related to its financial success. Similar, other studies reveal

that customer satisfaction has a positive impact on components of financial success

measures, such as company revenues (Ittner & Larcker, 1998; Gruca & Rego, 2005), cash

flow (Gruca & Rego, 2005), return on investment (Anderson & Fornell, 1994; Anderson

et al., 1997; Anderson & Mittal, 2000), return on assets (Hallowell, 1996), long-term

financial performance (Mittal et al., 2005), and shareholder value (Anderson et al., 2004;

Gruca & Rego, 2005; Fornell et al., 2006). It is argued that customer loyalty helps to

increase profits and profitability, and thus financial performance of companies (Reichheld

& Teal, 2001). Furthermore, there is evidence that new product performance has a strong

positive impact on company success (Ernst et al., 2011), on return on sales, and return

on assets (Kostopoulos et al., 2011). In addition, product quality and perceived service

quality as well as market share are identified as important drivers of return on sales

(Buzzell & Gale, 1987). Thus, we hypothesize the following relationship.

Hypothesis 4: Company performance has a positive impact on the financial success

of a company pursuing a mass customization strategy.

Method

This study attempts to investigate the mediating role of robust process design and

interaction competence with regard to the relationship between the solution space de-

velopment capability and company performance as well as financial success through

company performance. Methodologically, a survey approach was chosen to collect the

201

required data which was enriched with secondary objective financial data to test the set

of hypotheses.

Data collection and sample

For this study, a self-reported online questionnaire was used to gather information about

strategic capabilities for MC and organizational performance of B2B manufacturing com-

panies from Germany, Austria, and Switzerland. In developing the questionnaire, (1)

a group of academic experts commented the questionnaire, (2) a translate-retranslate

procedure was executed in order to ensure translation accuracy of the scales and items

taken from publications, and (3) a pretest with students and professionals from a market

research agency were conducted. Thus, we address potential issues of comprehension,

meaning, and clarity before the questionnaire was finalized (c.f. Churchill Jr, 1979; Dou-

glas & Craig, 2007).

To reach a representative sample of companies, we used the ORBIS database of the

provider Bureau van Dijk, which provides detailed information on companies across the

world. After defining search strings according to our research focus (NACE code; coun-

try; number of employees; contact data available; financial data available), the ORBIS

database provided information on 1865 companies. These companies were contacted via

telephone by a marketing research agency in the period from September to November

2013. In case that a companies’ representative declared his or her willingness to take

part in the survey, a link to the online questionnaire was provided by email. We held

out the prospect of a company-specific benchmark report as an incentive for participa-

tion. In order to increase the response rate, follow-up phone calls were conducted and

reminder emails were sent. To obtain a representative sample of companies pursuing MC

strategies, we placed a self-categorization scheme at the beginning of the questionnaire

and excluded pure mass producers from the survey. Overall, we receive 193 completed

questionnaires, corresponding to a response rate of 10.7%. A random sample compar-

ison of non-respondent companies with respondent companies reveals no differences in

employees, industry, and return on equity. Furthermore, no differences could be iden-

tified between early and late respondents with regard to these variables (Armstrong &

Overton, 1977).

202

The questionnaires were completed by designated managers with comprehensive

knowledge of the company and its processes and products. The categories of respon-

dents’ professional position and the distribution of the surveyed companies across NACE

divisions are reported in Table 3.1 and Table 3.2.

Table 3.1: Distribution of respondents’ positions

Department Percentage(n = 193)

Research & Development 28.5%Marketing / Sales 22.3%Corporate Planning / Controlling / Management 20.2%Manufacturing 18.7%Product Management 10.3%

Table 3.2: Distribution of surveyed companies across NACE divisions

NACECode

NACE division Percentage(n = 193)

28 Manufacturing of machinery and equipment 46.1%25 Manufacturing of fabricated metal products, except

machinery and equipment13.0%

22 Manufacturing of rubber and plastic products 12.4%26 Manufacturing of computer, electronic and optical

products10.4%

27 Manufacturing of electrical equipment 7.8%29 Manufacturing of motor vehicles, trailers and semi-

trailers6.2%

- Other devisions 4.1%

Measures

We used existing scales provided in literature which will be described in the follow-

ing. The questionnaire items, unless stated otherwise, were measured using seven-point

scales, anchored by 1 = strongly disagree and 7 = strongly agree.

Dependent variables: market performance, organizational performance and

financial success

Relaying on prior literature, we used two related scales to measure company performance.

First, we partly adapted a scale for market performance proposed by Irving (1995). This

203

subjective performance scale comprises seven items (anchors: 1 = significantly worse

and 7 = significantly better) which are oriented towards perceived customer success of a

company in the last three years relative to main competitors. Second, we adapted a six-

item scale for organizational performance, which comprises indicators measuring market

performance as well as criteria for economic success of a company (Langerak et al., 2004).

This scale measures how well a company has perceptually performed over the last year

relative to its main competitors, for example in terms of market share. These scales are

showcased in Appendix 1. Finally, following prior management research (Homburg &

Pflesser, 2000), financial success was operationalized using return on sales (RoS) as an

objective measure for a company’s financial performance.

Independent variables and mediators: capabilities for mass customization

We measured companies’ capabilities to pursue a mass customization strategy using six

formative indices proposed by Wellige and Steiner (2014). Two indices are included

to measure a company’s capability to initially design (ISSD, five items) and to adapt a

solution space (ASSD, four items). A company’s RPD capability was operationalized

using two indices for, one related to process robustness (PR, three items) and one related

to product design (PDPR, three items). These indices evaluate the extent to which

a company applies activities related to RPD, thus reflects the level of a company’s

RPD capability. Finally, the interaction competence capability (IC) of a company was

measured using two indices with four indicators for internal IC (IIC) and three indicators

for external IC (EIC). Participants were asked to report to which degree their company

applies certain IC related activities. The complete item list of indicators can be found

in Appendix 1.

Control variables

Following prior literature, we included industry-level and firm-level factors to account

for effects of environmental variables. We controlled for technological turbulence which

was operationalized using a scale proposed by Jaworski and Kohli (1993). The scale

tapped the perceived pace and extent of change in technology and the proportion of new

products which have been made possible through new technologies. Furthermore, we

controlled for market turbulence with a six-item measure that grasps the rate of change

204

in customer preferences (Jaworski & Kohli, 1993). We included six binary variables for

different NACE-divisions (0 = ”not pertaining to this industry” and 1 = ”pertaining

to this industry”) to control for industry-level effects on performance, given that some

companies may exhibit high performance merely because they are in more profitable

industries (Tan & Wang, 2010). To control for the effect of different degrees of offered

product variety a scale proposed by Al-Zu’bi and Tsinopoulos (2012) was included.

To control for company size, we used the number of full-time employees, as this measure

is commonly believed to influence processes related to SSD, such as success of product

development processes (Narver & Slater, 1990; Chandy & Tellis, 2000; Im & Work-

man Jr, 2004), innovation performance (Chiva & Alegre, 2009), and success of product

introductions (Capon et al., 1990). In order to ensure that we obtain a sample of com-

panies pursuing a MC strategy as well as to be able to control for the potential influence

of different degrees of customization, we integrated a self-selection schema and asked

participants to match their company’s product strategy with the best fitting categories,

which range from ”engineer to order” to ”mass production”. All items of these scales

are listed in Appendix 1.

Analyses

Prior to the analysis, the data was examined through various procedures for accuracy

of data entry, missing values, and accuracy of distributions. All values, means, and

standard deviations of each of the variables were inspected for plausibility and found to

be accurate. Four cases exhibit several missing values and were excluded from analysis.

For some cases, missing NACE-codes were investigated and added manually. We updated

the financial performance data in the event that more recent figures were available.

Therefore, we checked a German state database for annual reports (ebundesanzeiger.de)

and have conduced a web-based search for annual reports of the Austrian and Swiss

companies.

During data screening, one case was identified showing unengaged responses and

was therefore excluded from further analysis. To detect potential unidimensional out-

liers, z-scores for all reflectively measured variables were inspected using SPSS 22. Some

z-scores exhibited values above the threshold of 3.29 (RoS; Mark Perf 4; Mark Turb 3),

and thus were potential outliers (Tabachnick & Fidell, 2007). It can be expected for

205

larger sample sizes, as in this survey that some z-values are above the threshold (Tabach-

nick & Fidell, 2007). Since all of these variables exhibited not more than three cases with

values slightly larger than the threshold value, the influence of these cases might not

skew analyses and results to a large extent (Tabachnick & Fidell, 2007). Additionally,

we inspected the histograms and boxplots of the variables. With one exception, none

of the histograms revealed any unattached cases, thus suggesting that no substantial

outliers existed. For Mark Turb 3, the boxplot indicated some outliers. As this variable

was not imperatively mandatory for the following analysis, it was excluded. Distribu-

tions of all variables were inspected for potential deviation from normality. Therefore,

frequency histograms, expected normal probability plots, detrended normal probability

plots, and skewness and kurtosis were assessed using SPSS (Tabachnick & Fidell, 2007).

Results revealed non-normal distributions for the company size, which were transformed

afterwards (square root of the inverse number of employees) in order to achieve normal

distribution. Data was screened for multivariate outliers using the criterion of the Maha-

lanobis distance. Four cases could be identified as multivariate outliers and were deleted

(p < .001) (Tabachnick & Fidell, 2007). After this data screening procedure, 184 cases

remained for the following analyses.

An exploratory factor analysis (EFA) was performed through SPSS 22 on 21 reflec-

tive items. The factorability of the correlation matrix was indicated by a Kaiser-Meyer-

Olkin measure value of .872, several significant correlations in the correlation matrix,

and mainly small values among the off-diagonal elements in the anti-image correlation

matrix. Communality values exceeded the threshold of .45 (Tabachnick & Fidell, 2007)

except for one variable which was excluded (Mark Turb 4). Three factors were extracted

using principal components analysis with varimax rotation. Two variables showed a high

cross-loading between factors, and thus were excluded from further analysis (Org Perf 3;

Org Perf 4). With one exception Cronbachs’ alpha values were equal or higher than

.75 indicating that factors were internally consistent and well defined by the variables

(Nunnally & Bernstein, 1994). One factor, consisting of four variables (Mark Turb 1;

Mark Turb 2; Mark Turb 5; Tech Turb 3), showed a very low alpha value of .57, and

thus, was removed. The EFA revealed that most of the market performance and the

organizational performance variables loaded together on one factor. As this was theoret-

ically conclusive, the extracted factor was retained (hereafter: company performance).

Results of the EFA are reported in Appendix 2.

206

The scores for the six first-order constructs (and the three second-order constructs)

of the formative indices for SSD, RPD, and IC proposed by Wellige and Steiner

(2014) were estimated in multiple indicator and multiple causes (MIMIC) models

(Joreskog & Goldberger, 1975) using SmartPLS 2.0.M3. After excluding some indi-

cators due to non-significant weights (ISSD 3; EIC 2; PDPR 1), all remaining formative

indicators in the three models had significant weights (t-values obtained from a boot-

strapping procedure with 184 cases and 500 samples). The reflective parts of the three

models showed significant loadings of at least .74 and sufficient reliability indicated by

Cronbachs’ alpha values higher than .7 (Nunnally & Bernstein, 1994). Convergent va-

lidity was indicated by AVE values exceeding the recommended threshold value of .5

and composite reliabilities greater than the belonging AVE (Hair et al., 2010). For the

following analyses, we calculated composite variables for each of the three capability

dimensions using the two respective underlying sub-capabilities of SSD, RPD, and IC.

A test for outliers and non-normal distributions revealed no issues.

Measure reliability and validity were assessed using confirmatory factor analysis

(CFA). We included the independent variables and dependent variables. Some indicators

showed poor loadings, and thus were removed (Mark Perf 1; Mark Perf 3; Mark Perf 4;

Org Perf 2; Org Perf 3; Org Perf 4; Org Perf 6). Modification indices were consulted to

determine if there was an opportunity to improve the model. Calculated fit measures in-

dicated a sufficient fit (χ2 = 81.40, d.f. = 55, goodness of fit index (GFI) = .93, adjusted

goodness of fit index (AGFI) = .89, non-normed fit index (NNFI) = .98, comparative

fit index (CFI) = .98, root mean squared error of approximation (RMSEA) = .05, stan-

dardized root mean square residual (SRMR) = .04). Item loadings were significant (p <

.001). Average variance extracted (AVE) values were calculated to evaluate the conver-

gent validity. For all factors the AVE was well above the recommended threshold of .5.

Fornell-Larcker criterion was applied in order to test for discriminant validity (Fornell

& Larcker, 1981). All factors met the conditions imposed. To test for reliability in the

factors composite reliability (CR) was computed. In all cases the CR value was above

the threshold of .7, indicating reliability in the factors (Bagozzi & Yi, 1988). Results

confirm convergent and discriminant validity of the scales (for a complete record of the

CFA, see Appendix 3). For the following analysis, composite variables of the extracted

factors were used.

207

Descriptive statistics and correlations for the study variables are presented in Ta-

ble 3.3. To examine the hypothesized mediating role of the RPD and IC capabilities

of the relationship between the SSD capability and company performance, we applied

a multiple mediation analytical approach using SPSS 22. As multiple ordinary least

square (OLS) regressions were performed to test our hypotheses, preliminary checks

were conducted to ensure the lack of multicollinearity in the variables included in the

analyses. The calculated variance inflation factor (VIF) scores were all significantly

less than 10, indicating that multicollinearity in not in issue within the models (Neter

et al., 1996). Furthermore, we checked for linearity, normality, outliers, homoscedas-

ticity, and independence of residuals by inspecting the normal probability plots of the

regression standardized residual and scatterplots (Tabachnick & Fidell, 2007). There

was no evidence for violation of common assumptions for this type of regression.

208

Table3.3:

Des

crip

tive

stati

stic

san

dco

rrel

ati

on

s

Mea

nSD

12

34

56

78

910

11

12

13

14

15

1N

AC

Edev

isio

n22

0.12

0.33

1.00

2N

AC

Edev

isio

n25

0.14

0.34

-.15∗

1.00

3N

AC

Edev

isio

n26

0.10

0.30

-.12

-.13

1.00

4N

AC

Edev

isio

n27

0.08

0.27

-.11

-.11

-.09

1.00

5N

AC

Edev

isio

n29

0.05

0.23

-.09

-.10

-.08

-.07

1.00

6N

AC

Edev

isio

ns

30-3

20.

020.

15-.

05-.

06-.

05-.

04-.

041.

007

Tec

hnol

ogic

altu

rbule

nce

3.9

71.

22

-.14

-.07

.30∗∗

.02

-.01

-.02

1.00

8P

roduct

vari

ety

5.11

1.01

.07

.09

-.07

.09

-.22∗∗

-.08

.08

1.00

9F

irm

size

0.06

0.0

2.0

4-.

01

.08

-.06

.01

-.08

-.03

-.08

1.00

10SSD

capab

ilit

y4.

181.

21.0

9-.

05-.

04-.

06.0

3.1

1.1

7∗

.02

-.22∗∗

1.0

011

ICca

pabilit

y4.

211.

23.0

1-.

03.0

8.0

2.0

9.0

3.2

4∗∗

.14

-.08

.51∗∗

1.0

012

RP

Dca

pab

ilit

y4.

321.

25.1

1.0

4-.

12.0

6-.

08-.

05.1

1.1

1-.

04

.53∗∗

.44∗∗

1.0

013

Com

pan

yp

erfo

rman

ce4.

811.

06-.

06.1

1.0

3-.

05.0

0-.

03.0

6.2

7∗∗

-.18∗

.26∗∗

.35∗∗

.42∗∗

1.0

014

RoS

5.11

7.06

-.15∗

.10

.17∗

-.12

-.13

-.08

.09

-.05

.01

.04

-.04

.06

.13

1.0

015

Deg

ree

ofcu

stom

izati

on2.

381.

15-.

08-.

09.0

7.0

1-.

08.0

8-.

11

-.14

-.32∗∗

.15∗

-.03

-.06

-.06

.15

1.0

0

Note

s.∗ p

<.0

5,∗∗

p<

.01.

n=

184.

SD

=st

andar

ddev

iati

on.

209

To test the hypothesized mediating roles of the RPD and IC capabilities, we followed

the four step procedure suggested by Baron and Kenny (1986) as well as the more

recent analysis bootstrapping approaches for multiple mediation analysis (Preacher &

Hayes, 2008; Williams & MacKinnon, 2008). The additional bootstrapping approach

was chosen as investigating multiple mediation is more complex compared with simple

mediation for which Baron and Kenny’s approach is originally designed. Multiple

mediation analysis comprises not only the test for the indirect effects, but also to in-

vestigate potential overlaps between individual mediating effects that frequently bias

their effects on the dependent variable (West & Aiken, 1997; Preacher & Hayes, 2008).

Therefore, the total indirect effect and the specific indirect effect of each mediator are

investigated in the context of the multiple mediator model.

First, following the procedure suggested by Baron and Kenny (1986), we tested

the relationship between the predictor and the mediators as well as the relationships

between the mediators and company performance (see Table 3.4). Model 1 represents

the base-line model containing all control variables. The relationship between the SSD

capability and company performance was examined with Model 2. The coefficient for

the SSD capability indicate a positive relationship with company performance (β =

.23, SE = .06, p < .01). Thereby, the SSD capability explains an additional 5% of

the variance in company performance (R2 change = .06; F change (1, 172) = 12.59,

p < .01). Model 3 adds the mediators RPD capability and IC capability. As shown

in Model 3, the β-coefficients of the RPD capability and the IC capability are positive

and significant (β = .16, SE = .07, p < .05; β = .32, SE = .07, p < .001). The two

mediator variables additionally explained 14% of the variance in company performance

(R2 change = .14; F change (2, 170) = 16.80, p < .001). Results further show that the

relationship between the SSD capability and company performance becomes insignificant

when controlled by the mediators (β = -.03, SE = .07, p>.1). Two additional regression

analyses indicate that SSD is significantly positive related to the IC capability (β = .58,

SE = .07, p < .001) and to the RPD capability (β = .51, SE = .07, p < .001), see

Table 3.5. Furthermore, the relationship between company performance and RoS was

examined. Company performance records a statistically significant positive β-value (β

= .95, SE = .50, p < .1).

We estimated structural equation models using AMOS 22 to assess the fit of the

models. First, we analyzed a causal model without the mediating variables. Results

210

Table 3.4: Results of the hierarchical OLS regression analyses

Dependent variable: company performance

Model 1 Model 2 Model 3

Constant 4.14∗∗∗ (.64) 3.38∗∗∗ (.65) 2.87∗∗∗ (.61)

ControlsNACE devision 22 -.22 (.25) -.33 (.24) -.38 (.22)NACE devision 25 .21 (.23) .22 (.23) .16 (.21)NACE devision 26 .22 (.28) .30 (.27) .38 (.25)NACE devision 27 -.31 (.29) -.24 (.29) -.40 (.26)NACE devision 29 .24 (.35) .19 (.34) .24 (.32)NACE devisions 30-32 -.10 (.52) -.28 (.51) -.02 (.47)Firm size -9.02∗ (3.64) -7.09 (3.57) -8.62∗ (3.29)Technological turbulence 0 (.07) -.05 (.07) -.08 (.06)Product variety .27∗∗ (.08) .27∗∗ (.08) .22∗∗ (.71)Degree of customization -.07 (.07) -.11 (.07) -.09 (.07)

Independent variableSSD capability .23∗∗ (.06) -.03 (.07)

Mediator variablesIC capability .32∗∗∗ (.07)RPD capability .16∗ (.07)

R2 .13 .19 .32adjusted R2 .08 .13 .27F 2.48∗∗ 3.55∗∗∗ 6.14∗∗∗

F change 2.64∗∗ 12.59∗∗ 16.80∗∗∗

Notes. Unstandardized regression coefficients. Standard error in parentheses. n = 184.†p < .1, ∗p < .05, ∗∗p < .01, ∗∗∗p < .001.

reveal that the SSD capability has a significant positive effect on company performance

(β = .23, SE = .06 p < .001) and company performance is positively related to ROS (β

= .97, SE = .49, p < .05). With respect to the overall model fit, chi-square statistic (χ2

= 151.82, d.f. = 93), RMSEA (.06), SRMR (.04), and other global fit statistics (GFI =

.92, AGFI = .84, CFI = .95) indicate that the proposed model fits the data satisfactory.

Second, a causal model that includes the two mediators was specified and estimated.

The residuals of the mediators were free to covary in order to account for any covariances

among the mediators that were not regarded by the model (Preacher & Hayes, 2008).

The effect of the SSD capability on company performance turned out to be insignificant

(β = -.11, SE = .12, p> .1). Also, results show that the SSD capability has a significant

positive effect on the RPD capability (β = .63, SE = .08, p < .001) and on the IC

capability (β = .66, SE = .09, p < .001). Furthermore, the effects of the two capabilities

on company performance are positive and significant (β = .22, SE = .13 p < .1; β =

211

Table 3.5: Results of OLS regression analyses for mediating variables and RoS

Dependent variables:

IC RPD RoScapability capability

Constant 1.44∗ (.69) .37 (.69) -.98 (4.71)

ControlsNACE devision 22 .17 (.25) -.01 (.25) -2.72† (1.67)NACE devision 25 .17 (.24) .06 (.24) 1.61 (1.55)NACE devision 26 -.43 (.28) .37 (.29) 2.24 (1.84)NACE devision 27 .37 (.30) .23 (.30) -2.95 (1.96)NACE devision 29 -.50 (.36) .65† (.36) -4.38† (2.32)NACE devisions 30-32 -.85 (.54) .05 (.54) -4.72 (3.48)Firm size 3.71 (3.78) 2.32 (3.78) 20.93 (24.64)Technological turbulence .05 (.07) .13† (.07) .37 (.44)Product variety .05 (.08) .18∗ (.08) -.63 (.54)Degree of customization -.07 (.08) .03 (.07) 1.00∗ (.48)

Independent variableSSD capability .58∗∗∗ (.07) .51∗∗∗ (.07)Company performance .95† (.50)

R2 .34 .32 .13adjusted R2 .30 .28 .07F 8.95∗∗∗ 8.23∗∗∗ 2.15∗

Notes. Unstandardized regression coefficients. Standard error in parentheses.n = 184. †p < .1, ∗p < .05, ∗∗p < .01, ∗∗∗p < .001.

.39, SE = .07, p < .001). The model also indicates a positive effect from company

performance on RoS (β = .74, SE = .45, p < .1). Overall, the model fits the data

adequately (χ2 = 232.44, d.f. = 166, RMSEA = .05, SRMR = .05, GFI = .90, AGFI =

.84, CFI = .96) and confirms the results of the hierarchical regression analysis. According

to Baron and Kenny (1986), the results of the hierarchical regression analyses and

the SEM models provide evidence that the IC capability and the RPD capability fully

mediate the relationship between SSD and company performance.

Post hoc analysis

Although Baron and Kenny’s approach is frequently used to analyze mediation mod-

els, it has been criticized heavily for various reasons (e.g. MacKinnon et al., 2002; Fritz

& MacKinnon, 2007; Hayes, 2009). In order to provide additional proof for the hypoth-

esized model, an additional analytical approach was applied to further investigate the

212

hypothesized multiple mediation model. For this purpose, we chose a bootstrapping ap-

proach, relying on Preacher and Hayes’ (2008) SPSS macro to calculate the specific

indirect effects, the total indirect effect, and to obtain confidence intervals (CI) for these

effects. This nonparametric approach was preferred over a multivariate extension of So-

bel’s (1982) test as bootstrapping does not require the sample distribution of the total

and specific indirect effects to be normally distributed (Hayes, 2009; Hayes & Scharkow,

2013). As such a normal distribution is regularly not met in samples with n < 200 cases

(Preacher & Hayes, 2008), bootstrapping is generally regarded as superior to Sobel’s

test (Briggs, 2006; Williams & MacKinnon, 2008).

Results of the bootstrapping (see Table 3.6) provides specific indirect effects of

.08 through the RPD capability, and .18 through the IC capability with bias corrected

and accelerated bootstrap CI.95 of .01 to .16 (RPD) and .09 to .30 (IC). These results

reveal that both RPD and IC mediate the effect of SSD on company performance.

Furthermore, to examine the contrast of the two specific indirect effects, the pairwise

contrast was bias corrected and accelerated bootstrapped with a CI.95 of -.26 to .04.

This shows that the two mediators do not differ significantly in terms of the effect sizes,

thus the two mediators intervene the SSD capability-company performance relationship

equally (Preacher & Hayes, 2008). The point estimate of the total indirect effect of

SSD on company performance via the mediators, calculated as the sum of the specific

indirect effects, is .26. The bias corrected and accelerated bootstrap CI.95 of .16 to .38

reveals that this effect is significant, indicating a difference between the total effect and

the direct effect of the SSD capability on company performance. Overall, these results

provide evidence for the mediating role of the RPD capability and the IC capability on

its own and as a set, and thereby, confirms previous analyses.

Considering our hypotheses, different analyses provide support for the hypothesized

positive impact of SSD on company performance (H1). Furthermore, the data confirm

the hypothesized intervening roles of the RPD capability (H2) and the IC capability (H3)

with regard to the association between the SSD capability and company performance.

Furthermore, the OLS model reveals a positive and significant relationship between

company performance and RoS, thus support for H4.

In the main models investigating the mediations, the impact of industry affiliation

and technological turbulence on company performance is not significant, thus negligible.

213

Table 3.6: Results of the mediation analyses

BCa bootstrapping95% CI

Point estimate Lower Upper

Indirect effectsIC capability .18 .09 .30RPD capability .08 .01 .38TOTAL .26 .16 .38

ContrastIC capability

-.11 -.26 .04versusRPD capability

Notes. BCa: bias corrected and accelerated. CI: confidence interval.5000 bootstrapping samples.

With the exception of Model 2, there are significant negative impacts of company size on

company performance. Furthermore, in all three main models, product variety shows a

moderate significant impact on company performance. This might be explained by the

importance of addressing customer needs more precisely as frequently stated in literature

(Zhang & Tseng, 2009; Ellis, 2010).

Discussion and implications

Although many studies reveal that specific organizational activities, resources, and ca-

pabilities are necessary to pursue a MC strategy (Fogliatto et al., 2012; Salvador et al.,

2009; Wellige & Steiner, 2014), the relationships between these capabilities have not been

empirically investigated in a systematic manner, yet (Wellige & Steiner, 2014). Following

the literature on MC and contingency theory, our study suggests an organizational con-

figuration that relates three strategic capabilities and their belonging functional areas of

product development, manufacturing, and marketing to each other in order to explain

company performance and financial success. These three capabilities have previously

been proposed as most important for companies following a MC strategy (Salvador

et al., 2009; Wellige & Steiner, 2014).

On the basis of a multilevel investigation involving perceptual and objective data

214

from three different sources (managers, a database, and annual reports/ financial state-

ments), we provide evidence for the previously stated hypothesis that all three capa-

bilities for MC are required at the same time (Salvador et al., 2009; Wellige & Steiner,

2014). Beside this, findings support our proposed organizational configuration, which

argues that different dependent capabilities along the value chain are required to achieve

high level performance. Our results show that the SSD capability is positively related

to company performance, when the RPD and IC capabilities are not taken into account.

When these two capabilities are included, data reveals that the SSD capability is in-

directly positively related to company performance through the capabilities for RPD

and IC. This provides proof of the suggested mediating roles of the IC and RPD capa-

bilities, which relate to stages of the value chain downstream to product development

and the associated SSD capability. Furthermore, findings reveal a positive association

between the perceptual measured construct for company performance and financial suc-

cess, whereas the later data was gathered from a database or from annual reports and

financial statements of the companies.

Thereby, this study goes beyond the investigation of the performance effect of single

capabilities for MC. For example it investigates how the capability for flexible but effi-

cient manufacturing affects operational performance and investigates the relationships

between the required capabilities simultaneously (Wellige & Steiner, 2014). Further-

more, we provide an organizational configuration for MC companies. As stated in a

previous study, in contexts with multiple conflicting environmental demands and inter-

nal configuration tradeoffs, hence in contexts in which MC companies oftentimes are

acting in, it is difficult to conceptually derive a single valid organizational configuration

(van de Ven et al., 2013). Therefore, for the identification of a suitable configuration,

we go beyond the conceptional stage by applying empirical analysis, which is one of

the most appropriate approaches to discover feasible configurations (van de Ven et al.,

2013). Thereby, we complement existing research on organizational configurations for

MC.

Findings of this study demonstrate the importance of as well as the relations between

the three capabilities SSD, RPD, and IC to achieve company performance and financial

success. This raises the issue for managers how their companies can benefit from these

insights. Companies should be aware of the need to acquire all three capabilities for

MC due to their dependencies and relations. The SSD capability is required to generate

215

market intelligence about customers’ divers needs and to translate this information into

a high-variety product offering that allows addressing each single customer as accurate

as possible, thereby laying the foundation for company performance. Nevertheless, as

already stated in literature, our results reveal that the development of the SSD capability

alone is not sufficient. Pursuing a MC strategy also requires companies to develop an IC

capability in order to be able to exchange large amounts of information with customers

to specify a product according to their needs and a RPD capability in order to be able

to efficiently produce the wide range of different product variants on short lead times.

As our results suggest that capabilities are necessary in different functional areas of

companies that pursue a MC strategy, the development and implementation of them

may require top management commitment in order to ensure the necessary mutual

coordination between them.

Companies intending to improve the transition from their SSD capability to orga-

nizational performance by investing in the RPD capability or the IC capability should

decide on the basis of the marginal benefits associated with the investment. This is

suggested by our results as the relative magnitude of the specific indirect effects through

RPD and IC can statistically not be distinguished, thus, both are equally significant.

For example, a company possessing high levels of the RPD capability might obtain only

small improvements in process robustness by a given investment, due to the diminishing

marginal utility. Rather, the company should invest in its less developed IC capability,

thereby gaining a higher utility.

Companies should also pay increased intention to the proposed organizational con-

figuration and relations and dependencies between the three required capabilities. In

order to make the most efficient use of the positive relations from SSD capability on the

capabilities for RPD and IC, companies should develop and implement mechanisms that

help to increase exchange and connectedness between the product development depart-

ment and the functional areas of manufacturing and marketing, in order to benefit from

the positive effects of knowledge spillover and exchange. Therefore, managers should

foster the implementation of formal and informal organizational integration mechanisms

such as cross-functional interfaces and cooperation as well as social relations that are as-

sociated with combining and integrating knowledge (see Martinez & Jarillo, 1991; Jansen

et al., 2006, 2009; Brettel et al., 2011). The implementation of such formal and infor-

mal organizational integration mechanisms might lead to more complex ties between

216

the mentioned functional areas of a company pursuing a MC strategy. More complex

ties might also increase the organizational opacity and help to erect higher barriers for

imitation by the competitors. In conclusion, the implementation of such mechanisms

can provide the basis for more sustained competitive advantage. We suggest that fu-

ture research should examine the distinct impact of these mechanisms on the relations

between the functional areas.

This study also contributes to research on MC in multiple ways. First, this study

provides empirical evidence for the importance of the three capabilities in the B2B

context for the first time, thereby supporting recent theoretical work on the capabilities

for MC (e.g. Salvador et al., 2009; Wellige & Steiner, 2014).

Second, by considering different functional areas of companies pursuing MC strate-

gies and investigating the relationships between distinct capabilities related to these

functional areas, this study takes a more holistic perspective on companies pursuing a

MC strategy than previous research. Thereby, this study helps to theoretically anchor

MC in the organizational theory and suggests an organizational configuration reconcil-

ing environmental circumstances, strategy, and capabilities of different functional areas

of companies.

Third, our analyses are not limited to main effects, but take multiple indirect effects

into account, which extends research on MC. Here, we follow the recommendation of

Preacher and Hayes (2008) to test multiple mediators in a single multiple mediation

model. This approach often provides more precise results and reduces the probability

of parameter bias resulting from omitted mediators (Judd & Kenny, 1981), which is

frequently given in single mediation models (Preacher & Hayes, 2008). These single

mediation models, which are oftentimes used in studies, imply an effect of A on B to

be transmitted by one mediator only, which is questionable in most cases (Preacher &

Hayes, 2008), and increases the likelihood of not detecting a suppressor effect.

Overall, this study improves our theoretical understanding of underlying organi-

zational aspects and required capabilities for MC as well as our knowledge about the

relations between the necessary capabilities and their interfering effects on company

performance.

217

Limitations and further research

This study presents a first step towards understanding the interrelationships between

capabilities for MC strategies and company performance. The limitations of this study

suggest the need for future research.

First, this study is focused regionally on companies in Germany, Austria, and

Switzerland. These countries are renowned for the high technological standard of their

companies. Thus, for a more valid generalization of the results, this study needs to be

replicated using a sample of companies stemming from countries with lower technolog-

ical standards or alternatively by using a more heterogeneous sample with companies

from various geographical regions.

Second, the analysis of self-reported measurement is well established to test relation-

ships between theoretical constructs such as SSD, RPD, IC, and company performance.

However, complementing our findings by conducting qualitative research would help to

develop a more comprehensive picture of the interrelations between the capabilities and

company performance (cf. Chiva & Alegre, 2009). Furthermore, it is important to bear

in mind that our data are of cross-sectional nature, and therefore the ability to fully

understand the order of effects is limited, and furthermore, hard tests of causality are

precluded.

Third, we use subjective and objective data to measure company success in order

to address the warranted criticisms of using self-reports only (Venkatraman, 1989), and

to prevent potential problems with common method bias. Additionally, using both

measures allows to provide further proof for validity of the model. However, it needs to

be noticed that there is a time offset between the survey data and the objective financial

data, thereby limiting the explanatory power of the results. The available financial data

was extracted from the last available annual report of a company, within the sample

ranging from 2011-13. While it is likely that the conditions and circumstances of the

surveyed incumbent companies will not have changed essentially across the relatively

short time offset in our study, however, there is no guarantee for this. Conducting

a replication of this study using longitudinal data would allow addressing this issue.

Alternatively, repeating the analysis as soon as all financial data of the companies for

2013 are available would also allow addressing that concern.

218

Fourth, literature largely lacks to answer the question how to develop and imple-

ment these three capabilities simultaneously. Thus, it would be worthwhile that future

studies investigate the evolution of the development and implementation process of the-

ses capabilities and thereby document best practices. Again, we suggest to replicate

this study using longitudinal data. By following a given set of companies over time, the

multiple observations for each company would help to strengthen and deepen our under-

standing about the interplay between the capabilities for MC strategies as well as their

development and impact on company performance over time (Jansen et al., 2009). Here,

researchers should take the important microfoundations of the capability development

process and especially the influence of managers’ cognition and hierarchy into account,

which has been largely neglected in the field of organizational research (Gavetti, 2005).

Furthermore, as indicated by recent research on MC, the organizational structure has an

impact on a company’s ability to develop a manufacturing system capable to efficiently

produce high variety (Huang et al., 2010). Thus, future research might also investigate

the influence of the organizational structure on the other two as well as the simultaneous

development of the three capabilities.

Finally, although literature on configuration theory expects that only one or a few

optimal configurations for a strategic goal exist (Vorhies & Morgan, 2003; Wiklund &

Shepherd, 2005), we are not able to claim that the proposed configuration for MC is

ideal for achieving highest performance. Therefore, various existing configurations in the

context need to be compared and additional organizational variables need to be taken

into account (van de Ven et al., 2013). Thus, our configuration should rather be seen as

one feasible configuration. This provides ground for future studies.

219

Appendix

Appendix 1: Scale items for construct measures

Initial solution space development (ISSD) (Wellige & Steiner, 2014).

ISSD ref 1∗ Within the technical, legal, and economic boundaries of our prod-uct domain, we are able to define an initial set of customizableproduct attributes that meets the heterogeneous requirements ofa broad group of customers, so that almost every individual cus-tomer can find a suitable product.

ISSD ref 2∗ We have the capability to develop a thorough understanding ofthe technical requirements and the customer value-drivers beforemarket launch, which allows us to clearly define an initial set ofcustomizable product attributes that grasps the heterogeneity ofcustomer needs in our market.

ISSD ref 3∗ We have found ways to efficiently develop an initial set of cus-tomizable product attributes according to the specific situation ofour internal and external business environment that addresses alarge numbers of different customer requirements.

ISSD 1 We apply customer interviews to identify the customers’ key-value-attributes.

ISSD 2 We apply the lead user method to identify the customers’ key-value-attributes.

ISSD 3 We apply customer surveys to identify the customers’ key-value-attributes.

ISSD 4 We apply virtual prototypes to evaluate our customers’ acceptancetowards product concepts.

ISSD 5 We apply benchmarking to learn from our competitors when defin-ing our product offering.

Adaptive solution space development (ASSD) (Wellige & Steiner, 2014).

ASSD ref 1∗ We have defined processes to evaluate the quality of fit of theexisting range of product variants and to adapt our offering ifnecessary.

ASSD ref 2∗ We regularly check the fit of our existing set of product variantswith our customers’ expectations and revise, trim or extend theavailable product assortment accordingly.

ASSD ref 3∗ We have found ways to efficiently refine our product range on aregular basis in order to comply with changing customer needsand new technologies.


220


ASSD ref 4∗ We regularly re-examine our understanding of the technical re-quirements and the customer value-drivers of our market, so thatwe are at all times able to provide a set of product variants thatmeets the heterogeneous requirements of a broad group of cus-tomers in a specific product domain.

ASSD 1 We apply trend analysis in order to monitor changes in customerneeds.

ASSD 2 We have routines / processes for our sales staff to report changesin customer needs that they have observed.

ASSD 3 We analyze sales and revenue data of all product variants in orderto identify poorly performing variants.

ASSD 4 We analyze customer behavior in the configuration process in orderto identify potential pitfalls or shortcomings of our offering.

Process robustness (PR) (Wellige & Steiner, 2014).

PR ref 1 Our manufacturing processes are designed to efficiently handlesmall batch sizes and frequent changes of the production set-up.

PR ref 2 Due to the design of our manufacturing processes, frequentchanges of the production set-up do not induce significantly highercosts.

PR 1 We apply simultaneous / concurrent engineering for the develop-ment of new manufacturing processes.

PR 2 Our manufacturing processes can be reconfigured in a modularway.

PR 3 Our employees are trained so that they can be assigned flexiblywithin the manufacturing process.

Product design for process robustness (PDPR) (Wellige & Steiner, 2014).

PDPR ref 1∗ The product architecture has been carefully designed and imple-mented in order to be able to provide all product variants that areavailable in the solution space with near mass production efficiencyand at a constant level of quality.

PDPR ref 2∗ Our product architecture enables us to efficiently manufacture ourcomplete range of product variants, as if they all were the same.

PDPR 1 We apply interdisciplinary teams for the development of newproducts.

PDPR 2 We employ simultaneous / concurrent engineering for the devel-opment of new products.

PDPR 3 During the development of new products we give particular atten-tion to manufacturing requirements.


221


External interaction competence (EIC) (Wellige & Steiner, 2014).

EIC ref 1∗ Considering the high level of product variety that we offer ourcustomers, we are doing a good job in reducing choice complexity.

EIC ref 2∗ We have found ways to purposefully support our customers insearching, identifying, and specifying suitable product variants.

EIC ref 3∗ Our sales process offers customers a high degree of usability interms of product specification.

EIC 1 During the customization process, we offer extensive informationabout all product configuration options.

EIC 2 Customers can receive support from trained employees anytimethroughout the entire product customization process.

EIC 3 During the design of our customization process, we paid particu-lar attention to creating an easily understandable process for ourcustomers.

EIC 4 We help our customers to get a better technical understandingof our products by providing information about the interrelationsbetween choice options.

Internal interaction competence (IIC) (Wellige & Steiner, 2014).

IIC ref 1∗ We have found ways to efficiently deal with the increased inboundand outbound information flow of customer specific data that re-sults from the high level of product variety that we offer.

IIC ref 2∗ Our internal IT systems can effectively handle all relevant datafor customer-specific orders.

IIC ref 3∗ With the help of certain tools / methods / technology we can pro-vide our customers with real-time information (pricing, deliverydates, etc.) during the customization process.

IIC 1 We apply mechanisms / routines that allow us to track the statusof customer orders.

IIC 2 We apply mechanisms / routines that allow us to provide real-timefeedback concerning product availability anytime throughout theentire customization process.

IIC 3 We apply mechanisms / routines that allow us to provide real-timefeedback concerning product prices anytime throughout the entirecustomization process.

IIC 4 We provide technical support systems to assist our sales staff.


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Organizational performance (Langerak et al., 2004).

Please use the following scale to indicate your extent of agreementabout how well your firm has performed over the last year relativeto your main competitors on each of the performance indicatorsmentioned below.

Org Perf 1 Sales growth

Org Perf 2 Profitability

Org Perf 3 New product success

Org Perf 4 Sales share new products (i.e., products introduced in the last 5years)

Org Perf 5 Market share

Org Perf 6 RoI

Market performance (Irving, 1995; Homburg & Pflesser, 2000).

In the last three years, relative to your competitors, how has yourcompany performed with respect to ...

Mark Perf 1 Attracting new customers?

Mark Perf 2 Achieving customer satisfaction?

Mark Perf 3 Keeping current customers?

Mark Perf 4 Providing value for customers?

Mark Perf 5 Attaining desired growth of customer base?

Mark Perf 6 Securing market share?

Mark Perf 7 Attaining desired sales growth?

Market turbulence (Jaworski & Kohli, 1993).

Mark Turb 1 In our kind of business, customers’ product preferences changequite a bit over time.

Mark Turb 2 Our customers tend to look for new product all the time.

Mark Turb 3 Sometimes our customers are very price-sensitive, but on otheroccasions, price is relatively unimportant.

Mark Turb 4 We are witnessing demand for our products and services fromcustomers who never bought them before.

Mark Turb 5 New customers tend to have product-related needs that are differ-ent from those of our existing customers.

Mark Turb 6 We cater to many of the same customers that we used to in thepast.


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Technology turbulence (Jaworski & Kohli, 1993).

Tech Turb 1 The technology in our industry is changing rapidly.

Tech Turb 2 Technological changes provide big opportunities in our industry.

Tech Turb 3 It is very difficult to forecast where the technology in our industrywill be in the next 2 to 3 years.

Tech Turb 4 A large number of new product ideas have been made possiblethrough technological breakthroughs in our industry.

Tech Turb 5 Technological developments in our industry are rather minor.

Product variety (Jaworski & Kohli, 1993).

Please indicate your opinion of how your company compares to itscompetitors in your industry in terms of:

Prod Var 1 Range of products produced by existing facilities.

Prod Var 2 Scope of features offered to final customers.

Prod Var 3 Number of product lines.

Degree of customization according to (Lampel & Mintzberg, 1996).

Please select the strategic approach from the following list, whichcorresponds most with the business model of your company:

Mass pro-duction

We target the broadest possible group of customers, produce on aslarge a scale as possible, and distribute a single design commonlyto all. Customers have no direct influence over design, production,or even distribution decisions.

Build toforecast

We respond to the needs of different clusters of customers, buteach cluster remains aggregated. The offered products are stan-dardized within a narrow range of features. A basic product designis modified and multiplied to cover various product dimensions butnot at the request of individual customers.

Assemble toorder

Each customer gets his / her own configuration but constrainedby the range of available options. The basic product design isnot customized, and the components are all mass produced forthe aggregate market. Thus, products are made to order fromstandardized components, but assembly is customized for eachindividual customer.

Made to or-der

We present a product prototype to a potential customer and thenadapt or tailor it to the individual needs. Customization worksbackward to the fabrication stage but not to the design stage.

Engineer toorder

Customers’ needs influence decision making at all stages design,fabrication, assembly, and distribution. The product is truly en-gineered to order and all stages are largely customized.

Notes. ∗Reflectively measured items.

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Appendix 2: Results of the explorative factor analysis

Min Max Mean SD (1) (2)∗∗∗ (3) (4) α

Mark Perf 1 1 7 4.84 1.19 .78 .94Mark Perf 2 2 7 5.24 1.04 .73Mark Perf 3 3 7 5.28 1.06 .76Mark Perf 4 2 7 5.11 1.05 .70Mark Perf 5 1 7 4.80 1.26 .89Mark Perf 6 1 7 4.76 1.30 .89Mark Perf 7 1 7 4.79 1.33 .81Org Perf 1 1 7 4.69 1.26 .71Org Perf 2 1 7 4.59 1.29 .57Org Perf 3∗∗ 1 7 4.83 1.33Org Perf 4∗∗ 1 7 4.68 1.32Org Perf 5 2 7 4.61 1.26 .74Org Perf 6 2 7 4.63 1.18 .60

Mark Turb 1 1 7 4.44 1.41 .59 .57Mark Turb 2 1 7 3.90 1.55 .75Mark Turb 4∗ 1 7 3.48 1.32Mark Turb 5 1 7 4.17 1.60 .46Tech Turb 3 1 7 3.54 1.56 .53

Tech Turb 1 1 7 3.60 1.46 .67 .75Tech Turb 2 1 7 4.50 1.51 .83Tech Turb 4 1 7 3.80 1.54 .80

Prod Var 1 1 7 5.15 1.17 .89 .83Prod Var 2 1 7 5.20 1.10 .86Prod Var 3 1 7 4.97 1.25 .81

Notes. Principal component analysis with varimax rotation. n = 184.All factor loadings below .4 are not displayed.(1) = company performance. (2) = market turbulence.(3) = technological turbulence. (4) = product variety.∗Excluded due to low communality value. ∗∗Excluded due to highcross-loading. ∗∗∗Excluded due to low α-value.

Appendix 3: Results of the confirmatory factor analysis

CR AVE MSV ASV (1) (2) (3) (4)

(1) Company performance .92 .66 .22 .15 .82(2) SSD capability .82 .70 .48 .31 .31 .84(3) IC capability .97 .94 .35 .27 .47 .59 .97(4) RPD capability .78 .64 .48 .29 .37 .70 .49 .80

Notes. n = 184. CR = composite reliability. AVE = average varianceextracted. MSV = maximum shared variance. ASV = average sharedvariance. Square root of AVE on the diagonal.

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